文章目录
- 缺失值处理——拉格朗日插值法
- dataframe合并
- 索引上的合并
- 轴向连接
- 合并重叠数据
- 重塑层次化索引
- 长宽格式的转换
- 移除重复数据
- 利用函数或映射进行数据转换
- 数据标准化
- 替换值
- 重命名轴索引
- 离散化与面元划分
- 检测和过滤异常值
- 排列与随机采样
- 计算指标与哑变量
- 属性构造
- 字符串对象方法
- 正则表达式
- pandas中矢量化的字符串函数
- 示例：USDA食品数据库
GitHub: https://github.com/RealEmperor/Python-for-Data-Analysis
缺失值处理——拉格朗日插值法
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
from scipy.interpolate import lagrange # 导入拉格朗日插值函数np.random.seed(12345)
plt.rc('figure', figsize=(10, 6))inputfile = 'data/catering_sale.xls' # 销量数据路径
outputfile = 'data/sales.xls' # 输出数据路径data = pd.read_excel(inputfile) # 读入数据# 过滤异常值，将其变为空值
"""
data[u'销量'][(data[u'销量'] < 400) | (data[u'销量'] > 5000)] = None
上面这样写会有警告：
SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame如果要更改原始数据，请使用单一赋值操作（loc）：
data.loc[(data[u'销量'] < 400) | (data[u'销量'] > 5000), u'销量'] = None如果想要一个副本，请确保强制让 Pandas 创建副本：
error_data = data.copy()
error_data.loc[(error_data[u'销量'] < 400) | (error_data[u'销量'] > 5000), u'销量'] = None参考：https://www.jianshu.com/p/72274ccb647a
"""
data.loc[(data[u'销量'] < 400) | (data[u'销量'] > 5000), u'销量'] = None# 自定义列向量插值函数
# s为列向量，n为被插值的位置，k为取前后的数据个数，默认为5
def ployinterp_column(s, n, k=5):y = s[list(range(n - k, n)) + list(range(n + 1, n + 1 + k))] # 取数y = y[y.notnull()] # 剔除空值return lagrange(y.index, list(y))(n) # 插值并返回插值结果# 逐个元素判断是否需要插值
for i in data.columns:for j in range(len(data)):if (data[i].isnull())[j]: # 如果为空即插值。"""data[i][j] = ployinterp_column(data[i], j) 这样写会有警告：SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame"""data.loc[j, i] = ployinterp_column(data[i], j)data.to_excel(outputfile) # 输出结果，写入文件
dataframe合并
#dataframe合并
# 1
df1 = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],'data1': range(7)})
df2 = DataFrame({'key': ['a', 'b', 'd'],'data2': range(3)})
print(df1)
print(df2)
data1 key
0 0 b
1 1 b
2 2 a
3 3 c
4 4 a
5 5 a
6 6 bdata2 key
0 0 a
1 1 b
2 2 d
pd.merge(df1, df2)
|
data1
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key
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data2
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0
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0
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b
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1
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1
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1
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b
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1
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2
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6
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b
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3
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0
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4
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0
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pd.merge(df1, df2, on='key')
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data1
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key
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data2
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0
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0
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5
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0
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# 2
df3 = DataFrame({'lkey': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],'data1': range(7)})
df4 = DataFrame({'rkey': ['a', 'b', 'd'],'data2': range(3)})
pd.merge(df3, df4, left_on='lkey', right_on='rkey')
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data1
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lkey
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data2
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rkey
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0
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0
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b
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1
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6
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pd.merge(df1, df2, how='outer')
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data1
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key
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data2
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0
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0.0
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b
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1.0
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1
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1.0
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b
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1.0
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2
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6.0
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b
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1.0
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3
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2.0
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a
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0.0
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4
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4.0
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a
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0.0
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5
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5.0
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a
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0.0
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6
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3.0
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c
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NaN
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7
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NaN
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d
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2.0
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# 3
df1 = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'b'],'data1': range(6)})
df2 = DataFrame({'key': ['a', 'b', 'a', 'b', 'd'],'data2': range(5)})
print(df1)
print(df2)
data1 key
0 0 b
1 1 b
2 2 a
3 3 c
4 4 a
5 5 bdata2 key
0 0 a
1 1 b
2 2 a
3 3 b
4 4 d
pd.merge(df1, df2, on='key', how='left')
|
data1
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key
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data2
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0
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0
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b
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1.0
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1
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0
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b
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3.0
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2
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1
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b
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1.0
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3
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1
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b
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3.0
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4
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2
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a
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0.0
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5
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2
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a
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2.0
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6
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3
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c
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NaN
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7
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4
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a
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0.0
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8
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4
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a
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2.0
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9
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5
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b
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1.0
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10
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5
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b
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3.0
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pd.merge(df1, df2, how='inner')
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data1
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key
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data2
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0
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0
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b
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1
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1
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0
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b
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3
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2
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1
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b
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1
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3
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1
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b
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3
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4
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5
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b
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1
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5
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5
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b
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3
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6
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2
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a
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0
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7
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2
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a
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2
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8
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4
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a
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0
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9
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4
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a
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2
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# 4
left = DataFrame({'key1': ['foo', 'foo', 'bar'],'key2': ['one', 'two', 'one'],'lval': [1, 2, 3]})
right = DataFrame({'key1': ['foo', 'foo', 'bar', 'bar'],'key2': ['one', 'one', 'one', 'two'],'rval': [4, 5, 6, 7]})
pd.merge(left, right, on=['key1', 'key2'], how='outer')
|
key1
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key2
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lval
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rval
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0
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foo
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one
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1.0
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4.0
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1
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foo
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one
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1.0
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5.0
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2
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foo
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two
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2.0
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NaN
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3
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bar
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one
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3.0
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6.0
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4
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bar
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two
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NaN
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7.0
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# 5
pd.merge(left, right, on='key1')
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key1
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key2_x
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lval
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key2_y
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rval
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0
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foo
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one
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1
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one
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4
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1
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foo
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one
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1
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one
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5
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2
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foo
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two
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2
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one
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4
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3
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foo
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two
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2
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one
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5
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4
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bar
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one
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3
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one
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6
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5
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bar
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one
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3
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two
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7
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pd.merge(left, right, on='key1', suffixes=('_left', '_right'))
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key1
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key2_left
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lval
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key2_right
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rval
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0
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foo
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one
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1
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one
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4
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1
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foo
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one
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1
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one
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5
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2
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foo
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two
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2
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one
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4
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3
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foo
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two
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2
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one
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5
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4
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bar
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one
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3
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one
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6
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5
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bar
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one
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3
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two
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7
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索引上的合并
# 1
left1 = DataFrame({'key': ['a', 'b', 'a', 'a', 'b', 'c'], 'value': range(6)})
right1 = DataFrame({'group_val': [3.5, 7]}, index=['a', 'b'])
print(left1)
print(right1)
key value
0 a 0
1 b 1
2 a 2
3 a 3
4 b 4
5 c 5group_val
a 3.5
b 7.0
pd.merge(left1, right1, left_on='key', right_index=True)
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key
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value
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group_val
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0
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a
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0
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3.5
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2
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a
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2
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3.5
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3
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a
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3
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3.5
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1
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b
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1
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7.0
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4
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b
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4
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7.0
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pd.merge(left1, right1, left_on='key', right_index=True, how='outer')
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key
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value
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group_val
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0
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a
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0
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3.5
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2
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a
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2
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3.5
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3
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a
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3
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3.5
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1
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b
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1
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7.0
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4
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b
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4
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7.0
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5
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c
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5
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NaN
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# 2
lefth = DataFrame({'key1': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],'key2': [2000, 2001, 2002, 2001, 2002],'data': np.arange(5.)})
righth = DataFrame(np.arange(12).reshape((6, 2)),index=[['Nevada', 'Nevada', 'Ohio', 'Ohio', 'Ohio', 'Ohio'],[2001, 2000, 2000, 2000, 2001, 2002]],columns=['event1', 'event2'])
print(lefth)
print(righth)
data key1 key2
0 0.0 Ohio 2000
1 1.0 Ohio 2001
2 2.0 Ohio 2002
3 3.0 Nevada 2001
4 4.0 Nevada 2002event1 event2
Nevada 2001 0 12000 2 3
Ohio 2000 4 52000 6 72001 8 92002 10 11
pd.merge(lefth, righth, left_on=['key1', 'key2'], right_index=True)
|
data
|
key1
|
key2
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event1
|
event2
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0
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0.0
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Ohio
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2000
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4
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5
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0
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0.0
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Ohio
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2000
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6
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7
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1
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1.0
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Ohio
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2001
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8
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9
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2
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2.0
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Ohio
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2002
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10
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11
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3
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3.0
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Nevada
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2001
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0
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1
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pd.merge(lefth, righth, left_on=['key1', 'key2'],right_index=True, how='outer')
|
data
|
key1
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key2
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event1
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event2
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0
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0.0
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Ohio
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2000
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4.0
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5.0
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0
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0.0
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Ohio
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2000
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6.0
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7.0
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1
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1.0
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Ohio
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2001
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8.0
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9.0
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2
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2.0
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Ohio
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2002
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10.0
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11.0
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3
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3.0
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Nevada
|
2001
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0.0
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1.0
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4
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4.0
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Nevada
|
2002
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NaN
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NaN
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4
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NaN
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Nevada
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2000
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2.0
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3.0
|
left2 = DataFrame([[1., 2.], [3., 4.], [5., 6.]], index=['a', 'c', 'e'],columns=['Ohio', 'Nevada'])
right2 = DataFrame([[7., 8.], [9., 10.], [11., 12.], [13, 14]],index=['b', 'c', 'd', 'e'], columns=['Missouri', 'Alabama'])
print(left2)
print(right2)
Ohio Nevada
a 1.0 2.0
c 3.0 4.0
e 5.0 6.0Missouri Alabama
b 7.0 8.0
c 9.0 10.0
d 11.0 12.0
e 13.0 14.0
pd.merge(left2, right2, how='outer', left_index=True, right_index=True)
|
Ohio
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Nevada
|
Missouri
|
Alabama
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a
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1.0
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2.0
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NaN
|
NaN
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b
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NaN
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NaN
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7.0
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8.0
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|
c
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3.0
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4.0
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9.0
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10.0
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d
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NaN
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NaN
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11.0
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12.0
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e
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5.0
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6.0
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13.0
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14.0
|
# 3
left2.join(right2, how='outer')
|
Ohio
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Nevada
|
Missouri
|
Alabama
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a
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1.0
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2.0
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NaN
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NaN
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b
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NaN
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NaN
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7.0
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8.0
|
|
c
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3.0
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4.0
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9.0
|
10.0
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d
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NaN
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NaN
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11.0
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12.0
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e
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5.0
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6.0
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13.0
|
14.0
|
left1.join(right1, on='key')
|
key
|
value
|
group_val
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0
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a
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0
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3.5
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1
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b
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1
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7.0
|
|
2
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a
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2
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3.5
|
|
3
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a
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3
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3.5
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4
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b
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4
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7.0
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5
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c
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5
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NaN
|
# 4
another = DataFrame([[7., 8.], [9., 10.], [11., 12.], [16., 17.]],index=['a', 'c', 'e', 'f'], columns=['New York', 'Oregon'])
left2.join([right2, another])
|
Ohio
|
Nevada
|
Missouri
|
Alabama
|
New York
|
Oregon
|
|
a
|
1.0
|
2.0
|
NaN
|
NaN
|
7.0
|
8.0
|
|
c
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3.0
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4.0
|
9.0
|
10.0
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9.0
|
10.0
|
|
e
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5.0
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6.0
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13.0
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14.0
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11.0
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12.0
|
left2.join([right2, another], how='outer')
|
Ohio
|
Nevada
|
Missouri
|
Alabama
|
New York
|
Oregon
|
|
a
|
1.0
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2.0
|
NaN
|
NaN
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7.0
|
8.0
|
|
b
|
NaN
|
NaN
|
7.0
|
8.0
|
NaN
|
NaN
|
|
c
|
3.0
|
4.0
|
9.0
|
10.0
|
9.0
|
10.0
|
|
d
|
NaN
|
NaN
|
11.0
|
12.0
|
NaN
|
NaN
|
|
e
|
5.0
|
6.0
|
13.0
|
14.0
|
11.0
|
12.0
|
|
f
|
NaN
|
NaN
|
NaN
|
NaN
|
16.0
|
17.0
|
轴向连接
# 1
arr = np.arange(12).reshape((3, 4))
print(arr)np.concatenate([arr, arr], axis=1)
[[ 0 1 2 3][ 4 5 6 7][ 8 9 10 11]]array([[ 0, 1, 2, 3, 0, 1, 2, 3],[ 4, 5, 6, 7, 4, 5, 6, 7],[ 8, 9, 10, 11, 8, 9, 10, 11]])
# 2
s1 = Series([0, 1], index=['a', 'b'])
s2 = Series([2, 3, 4], index=['c', 'd', 'e'])
s3 = Series([5, 6], index=['f', 'g'])pd.concat([s1, s2, s3])
a 0
b 1
c 2
d 3
e 4
f 5
g 6
dtype: int64
pd.concat([s1, s2, s3], axis=0)
a 0
b 1
c 2
d 3
e 4
f 5
g 6
dtype: int64
pd.concat([s1, s2, s3], axis=1)
|
0
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1
|
2
|
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a
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0.0
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NaN
|
NaN
|
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b
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1.0
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NaN
|
NaN
|
|
c
|
NaN
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2.0
|
NaN
|
|
d
|
NaN
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3.0
|
NaN
|
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e
|
NaN
|
4.0
|
NaN
|
|
f
|
NaN
|
NaN
|
5.0
|
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g
|
NaN
|
NaN
|
6.0
|
s4 = pd.concat([s1 * 5, s3])
print(s4)
a 0
b 5
f 5
g 6
dtype: int64
pd.concat([s1, s4], axis=1)
|
0
|
1
|
|
a
|
0.0
|
0
|
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b
|
1.0
|
5
|
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f
|
NaN
|
5
|
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g
|
NaN
|
6
|
pd.concat([s1, s4], axis=1, join='inner')
pd.concat([s1, s4], axis=1, join_axes=[['a', 'c', 'b', 'e']])
|
0
|
1
|
|
a
|
0.0
|
0.0
|
|
c
|
NaN
|
NaN
|
|
b
|
1.0
|
5.0
|
|
e
|
NaN
|
NaN
|
# 3
result = pd.concat([s1, s1, s3], keys=['one', 'two', 'three'])
print(result)
one a 0b 1
two a 0b 1
three f 5g 6
dtype: int64
result.unstack()
|
a
|
b
|
f
|
g
|
|
one
|
0.0
|
1.0
|
NaN
|
NaN
|
|
two
|
0.0
|
1.0
|
NaN
|
NaN
|
|
three
|
NaN
|
NaN
|
5.0
|
6.0
|
# 4
pd.concat([s1, s2, s3], axis=1, keys=['one', 'two', 'three'])
|
one
|
two
|
three
|
|
a
|
0.0
|
NaN
|
NaN
|
|
b
|
1.0
|
NaN
|
NaN
|
|
c
|
NaN
|
2.0
|
NaN
|
|
d
|
NaN
|
3.0
|
NaN
|
|
e
|
NaN
|
4.0
|
NaN
|
|
f
|
NaN
|
NaN
|
5.0
|
|
g
|
NaN
|
NaN
|
6.0
|
df1 = DataFrame(np.arange(6).reshape(3, 2), index=['a', 'b', 'c'],columns=['one', 'two'])
df2 = DataFrame(5 + np.arange(4).reshape(2, 2), index=['a', 'c'],columns=['three', 'four'])
pd.concat([df1, df2], axis=1, keys=['level1', 'level2'])
|
level1
|
level2
|
|
one
|
two
|
three
|
four
|
|
a
|
0
|
1
|
5.0
|
6.0
|
|
b
|
2
|
3
|
NaN
|
NaN
|
|
c
|
4
|
5
|
7.0
|
8.0
|
pd.concat({'level1': df1, 'level2': df2}, axis=1)
|
level1
|
level2
|
|
one
|
two
|
three
|
four
|
|
a
|
0
|
1
|
5.0
|
6.0
|
|
b
|
2
|
3
|
NaN
|
NaN
|
|
c
|
4
|
5
|
7.0
|
8.0
|
pd.concat([df1, df2], axis=1, keys=['level1', 'level2'],names=['upper', 'lower'])
|
upper
|
level1
|
level2
|
|
lower
|
one
|
two
|
three
|
four
|
|
a
|
0
|
1
|
5.0
|
6.0
|
|
b
|
2
|
3
|
NaN
|
NaN
|
|
c
|
4
|
5
|
7.0
|
8.0
|
# 5
df1 = DataFrame(np.random.randn(3, 4), columns=['a', 'b', 'c', 'd'])
df2 = DataFrame(np.random.randn(2, 3), columns=['b', 'd', 'a'])
print(df1)
print(df2)
a b c d
0 -0.204708 0.478943 -0.519439 -0.555730
1 1.965781 1.393406 0.092908 0.281746
2 0.769023 1.246435 1.007189 -1.296221b d a
0 0.274992 0.228913 1.352917
1 0.886429 -2.001637 -0.371843
pd.concat([df1, df2], ignore_index=True)
|
a
|
b
|
c
|
d
|
|
0
|
-0.204708
|
0.478943
|
-0.519439
|
-0.555730
|
|
1
|
1.965781
|
1.393406
|
0.092908
|
0.281746
|
|
2
|
0.769023
|
1.246435
|
1.007189
|
-1.296221
|
|
3
|
1.352917
|
0.274992
|
NaN
|
0.228913
|
|
4
|
-0.371843
|
0.886429
|
NaN
|
-2.001637
|
合并重叠数据
# 1
a = Series([np.nan, 2.5, np.nan, 3.5, 4.5, np.nan],index=['f', 'e', 'd', 'c', 'b', 'a'])
b = Series(np.arange(len(a), dtype=np.float64),index=['f', 'e', 'd', 'c', 'b', 'a'])
b[-1] = np.nanprint(a)
print(b)np.where(pd.isnull(a), b, a)
f NaN
e 2.5
d NaN
c 3.5
b 4.5
a NaN
dtype: float64
f 0.0
e 1.0
d 2.0
c 3.0
b 4.0
a NaN
dtype: float64array([ 0. , 2.5, 2. , 3.5, 4.5, nan])
# 2
b[:-2].combine_first(a[2:])
a NaN
b 4.5
c 3.0
d 2.0
e 1.0
f 0.0
dtype: float64
# 3
df1 = DataFrame({'a': [1., np.nan, 5., np.nan],'b': [np.nan, 2., np.nan, 6.],'c': range(2, 18, 4)})
df2 = DataFrame({'a': [5., 4., np.nan, 3., 7.],'b': [np.nan, 3., 4., 6., 8.]})
df1.combine_first(df2)
|
a
|
b
|
c
|
|
0
|
1.0
|
NaN
|
2.0
|
|
1
|
4.0
|
2.0
|
6.0
|
|
2
|
5.0
|
4.0
|
10.0
|
|
3
|
3.0
|
6.0
|
14.0
|
|
4
|
7.0
|
8.0
|
NaN
|
重塑层次化索引
# 1
data = DataFrame(np.arange(6).reshape((2, 3)),index=pd.Index(['Ohio', 'Colorado'], name='state'),columns=pd.Index(['one', 'two', 'three'], name='number'))
print(data)
number one two three
state
Ohio 0 1 2
Colorado 3 4 5
# stack 把 column 转成 index
result = data.stack()
print(result)
state number
Ohio one 0two 1three 2
Colorado one 3two 4three 5
dtype: int32
# unstack 把 index 转成 column
result.unstack()
|
number
|
one
|
two
|
three
|
|
state
|
|
|
|
|
Ohio
|
0
|
1
|
2
|
|
Colorado
|
3
|
4
|
5
|
result.unstack(0)
|
state
|
Ohio
|
Colorado
|
|
number
|
|
|
|
one
|
0
|
3
|
|
two
|
1
|
4
|
|
three
|
2
|
5
|
result.unstack('state')
|
state
|
Ohio
|
Colorado
|
|
number
|
|
|
|
one
|
0
|
3
|
|
two
|
1
|
4
|
|
three
|
2
|
5
|
# 2
s1 = Series([0, 1, 2, 3], index=['a', 'b', 'c', 'd'])
s2 = Series([4, 5, 6], index=['c', 'd', 'e'])
data2 = pd.concat([s1, s2], keys=['one', 'two'])
print(data2)
one a 0b 1c 2d 3
two c 4d 5e 6
dtype: int64
# data2.stack() # 没有column 执行会报错
data2.unstack()
|
a
|
b
|
c
|
d
|
e
|
|
one
|
0.0
|
1.0
|
2.0
|
3.0
|
NaN
|
|
two
|
NaN
|
NaN
|
4.0
|
5.0
|
6.0
|
data2.unstack().stack()
one a 0.0b 1.0c 2.0d 3.0
two c 4.0d 5.0e 6.0
dtype: float64
data2.unstack().stack(dropna=False)
one a 0.0b 1.0c 2.0d 3.0e NaN
two a NaNb NaNc 4.0d 5.0e 6.0
dtype: float64
# 3
df = DataFrame({'left': result, 'right': result + 5},columns=pd.Index(['left', 'right'], name='side'))
print(df)
side left right
state number
Ohio one 0 5two 1 6three 2 7
Colorado one 3 8two 4 9three 5 10
df.unstack('state')
|
side
|
left
|
right
|
|
state
|
Ohio
|
Colorado
|
Ohio
|
Colorado
|
|
number
|
|
|
|
|
|
one
|
0
|
3
|
5
|
8
|
|
two
|
1
|
4
|
6
|
9
|
|
three
|
2
|
5
|
7
|
10
|
df.unstack('state').stack('side')
|
state
|
Colorado
|
Ohio
|
|
number
|
side
|
|
|
|
one
|
left
|
3
|
0
|
|
right
|
8
|
5
|
|
two
|
left
|
4
|
1
|
|
right
|
9
|
6
|
|
three
|
left
|
5
|
2
|
|
right
|
10
|
7
|
长宽格式的转换
data = pd.read_csv('data/macrodata.csv')
print(data)
year quarter realgdp realcons realinv realgovt realdpi \
0 1959.0 1.0 2710.349 1707.4 286.898 470.045 1886.9
1 1959.0 2.0 2778.801 1733.7 310.859 481.301 1919.7
2 1959.0 3.0 2775.488 1751.8 289.226 491.260 1916.4
3 1959.0 4.0 2785.204 1753.7 299.356 484.052 1931.3
4 1960.0 1.0 2847.699 1770.5 331.722 462.199 1955.5
5 1960.0 2.0 2834.390 1792.9 298.152 460.400 1966.1
6 1960.0 3.0 2839.022 1785.8 296.375 474.676 1967.8
7 1960.0 4.0 2802.616 1788.2 259.764 476.434 1966.6
8 1961.0 1.0 2819.264 1787.7 266.405 475.854 1984.5
9 1961.0 2.0 2872.005 1814.3 286.246 480.328 2014.4
10 1961.0 3.0 2918.419 1823.1 310.227 493.828 2041.9
11 1961.0 4.0 2977.830 1859.6 315.463 502.521 2082.0
12 1962.0 1.0 3031.241 1879.4 334.271 520.960 2101.7
13 1962.0 2.0 3064.709 1902.5 331.039 523.066 2125.2
14 1962.0 3.0 3093.047 1917.9 336.962 538.838 2137.0
15 1962.0 4.0 3100.563 1945.1 325.650 535.912 2154.6
16 1963.0 1.0 3141.087 1958.2 343.721 522.917 2172.5
17 1963.0 2.0 3180.447 1976.9 348.730 518.108 2193.1
18 1963.0 3.0 3240.332 2003.8 360.102 546.893 2217.9
19 1963.0 4.0 3264.967 2020.6 364.534 532.383 2254.6
20 1964.0 1.0 3338.246 2060.5 379.523 529.686 2299.6
21 1964.0 2.0 3376.587 2096.7 377.778 526.175 2362.1
22 1964.0 3.0 3422.469 2135.2 386.754 522.008 2392.7
23 1964.0 4.0 3431.957 2141.2 389.910 514.603 2420.4
24 1965.0 1.0 3516.251 2188.8 429.145 508.006 2447.4
25 1965.0 2.0 3563.960 2213.0 429.119 508.931 2474.5
26 1965.0 3.0 3636.285 2251.0 444.444 529.446 2542.6
27 1965.0 4.0 3724.014 2314.3 446.493 544.121 2594.1
28 1966.0 1.0 3815.423 2348.5 484.244 556.593 2618.4
29 1966.0 2.0 3828.124 2354.5 475.408 571.371 2624.7
.. ... ... ... ... ... ... ...
173 2002.0 2.0 11538.770 7997.8 1810.779 774.408 8658.9
174 2002.0 3.0 11596.430 8052.0 1814.531 786.673 8629.2
175 2002.0 4.0 11598.824 8080.6 1813.219 799.967 8649.6
176 2003.0 1.0 11645.819 8122.3 1813.141 800.196 8681.3
177 2003.0 2.0 11738.706 8197.8 1823.698 838.775 8812.5
178 2003.0 3.0 11935.461 8312.1 1889.883 839.598 8935.4
179 2003.0 4.0 12042.817 8358.0 1959.783 845.722 8986.4
180 2004.0 1.0 12127.623 8437.6 1970.015 856.570 9025.9
181 2004.0 2.0 12213.818 8483.2 2055.580 861.440 9115.0
182 2004.0 3.0 12303.533 8555.8 2082.231 876.385 9175.9
183 2004.0 4.0 12410.282 8654.2 2125.152 865.596 9303.4
184 2005.0 1.0 12534.113 8719.0 2170.299 869.204 9189.6
185 2005.0 2.0 12587.535 8802.9 2131.468 870.044 9253.0
186 2005.0 3.0 12683.153 8865.6 2154.949 890.394 9308.0
187 2005.0 4.0 12748.699 8888.5 2232.193 875.557 9358.7
188 2006.0 1.0 12915.938 8986.6 2264.721 900.511 9533.8
189 2006.0 2.0 12962.462 9035.0 2261.247 892.839 9617.3
190 2006.0 3.0 12965.916 9090.7 2229.636 892.002 9662.5
191 2006.0 4.0 13060.679 9181.6 2165.966 894.404 9788.8
192 2007.0 1.0 13099.901 9265.1 2132.609 882.766 9830.2
193 2007.0 2.0 13203.977 9291.5 2162.214 898.713 9842.7
194 2007.0 3.0 13321.109 9335.6 2166.491 918.983 9883.9
195 2007.0 4.0 13391.249 9363.6 2123.426 925.110 9886.2
196 2008.0 1.0 13366.865 9349.6 2082.886 943.372 9826.8
197 2008.0 2.0 13415.266 9351.0 2026.518 961.280 10059.0
198 2008.0 3.0 13324.600 9267.7 1990.693 991.551 9838.3
199 2008.0 4.0 13141.920 9195.3 1857.661 1007.273 9920.4
200 2009.0 1.0 12925.410 9209.2 1558.494 996.287 9926.4
201 2009.0 2.0 12901.504 9189.0 1456.678 1023.528 10077.5
202 2009.0 3.0 12990.341 9256.0 1486.398 1044.088 10040.6 cpi m1 tbilrate unemp pop infl realint
0 28.980 139.7 2.82 5.8 177.146 0.00 0.00
1 29.150 141.7 3.08 5.1 177.830 2.34 0.74
2 29.350 140.5 3.82 5.3 178.657 2.74 1.09
3 29.370 140.0 4.33 5.6 179.386 0.27 4.06
4 29.540 139.6 3.50 5.2 180.007 2.31 1.19
5 29.550 140.2 2.68 5.2 180.671 0.14 2.55
6 29.750 140.9 2.36 5.6 181.528 2.70 -0.34
7 29.840 141.1 2.29 6.3 182.287 1.21 1.08
8 29.810 142.1 2.37 6.8 182.992 -0.40 2.77
9 29.920 142.9 2.29 7.0 183.691 1.47 0.81
10 29.980 144.1 2.32 6.8 184.524 0.80 1.52
11 30.040 145.2 2.60 6.2 185.242 0.80 1.80
12 30.210 146.4 2.73 5.6 185.874 2.26 0.47
13 30.220 146.5 2.78 5.5 186.538 0.13 2.65
14 30.380 146.7 2.78 5.6 187.323 2.11 0.67
15 30.440 148.3 2.87 5.5 188.013 0.79 2.08
16 30.480 149.7 2.90 5.8 188.580 0.53 2.38
17 30.690 151.3 3.03 5.7 189.242 2.75 0.29
18 30.750 152.6 3.38 5.5 190.028 0.78 2.60
19 30.940 153.7 3.52 5.6 190.668 2.46 1.06
20 30.950 154.8 3.51 5.5 191.245 0.13 3.38
21 31.020 156.8 3.47 5.2 191.889 0.90 2.57
22 31.120 159.2 3.53 5.0 192.631 1.29 2.25
23 31.280 160.7 3.76 5.0 193.223 2.05 1.71
24 31.380 162.0 3.93 4.9 193.709 1.28 2.65
25 31.580 163.1 3.84 4.7 194.303 2.54 1.30
26 31.650 166.0 3.93 4.4 194.997 0.89 3.04
27 31.880 169.1 4.35 4.1 195.539 2.90 1.46
28 32.280 171.8 4.62 3.9 195.999 4.99 -0.37
29 32.450 170.3 4.65 3.8 196.560 2.10 2.55
.. ... ... ... ... ... ... ...
173 180.000 1199.5 1.70 5.8 288.028 1.56 0.14
174 181.200 1204.0 1.61 5.7 288.783 2.66 -1.05
175 182.600 1226.8 1.20 5.8 289.421 3.08 -1.88
176 183.200 1248.4 1.14 5.9 290.019 1.31 -0.17
177 183.700 1287.9 0.96 6.2 290.704 1.09 -0.13
178 184.900 1297.3 0.94 6.1 291.449 2.60 -1.67
179 186.300 1306.1 0.90 5.8 292.057 3.02 -2.11
180 187.400 1332.1 0.94 5.7 292.635 2.35 -1.42
181 189.100 1340.5 1.21 5.6 293.310 3.61 -2.41
182 190.800 1361.0 1.63 5.4 294.066 3.58 -1.95
183 191.800 1366.6 2.20 5.4 294.741 2.09 0.11
184 193.800 1357.8 2.69 5.3 295.308 4.15 -1.46
185 194.700 1366.6 3.01 5.1 295.994 1.85 1.16
186 199.200 1375.0 3.52 5.0 296.770 9.14 -5.62
187 199.400 1380.6 4.00 4.9 297.435 0.40 3.60
188 200.700 1380.5 4.51 4.7 298.061 2.60 1.91
189 202.700 1369.2 4.82 4.7 298.766 3.97 0.85
190 201.900 1369.4 4.90 4.7 299.593 -1.58 6.48
191 203.574 1373.6 4.92 4.4 300.320 3.30 1.62
192 205.920 1379.7 4.95 4.5 300.977 4.58 0.36
193 207.338 1370.0 4.72 4.5 301.714 2.75 1.97
194 209.133 1379.2 4.00 4.7 302.509 3.45 0.55
195 212.495 1377.4 3.01 4.8 303.204 6.38 -3.37
196 213.997 1384.0 1.56 4.9 303.803 2.82 -1.26
197 218.610 1409.3 1.74 5.4 304.483 8.53 -6.79
198 216.889 1474.7 1.17 6.0 305.270 -3.16 4.33
199 212.174 1576.5 0.12 6.9 305.952 -8.79 8.91
200 212.671 1592.8 0.22 8.1 306.547 0.94 -0.71
201 214.469 1653.6 0.18 9.2 307.226 3.37 -3.19
202 216.385 1673.9 0.12 9.6 308.013 3.56 -3.44 [203 rows x 14 columns]
periods = pd.PeriodIndex(year=data.year, quarter=data.quarter, name='date')
print(periods)
PeriodIndex(['1959Q1', '1959Q2', '1959Q3', '1959Q4', '1960Q1', '1960Q2','1960Q3', '1960Q4', '1961Q1', '1961Q2',...'2007Q2', '2007Q3', '2007Q4', '2008Q1', '2008Q2', '2008Q3','2008Q4', '2009Q1', '2009Q2', '2009Q3'],dtype='period[Q-DEC]', name='date', length=203, freq='Q-DEC')
data = DataFrame(data.to_records(),columns=pd.Index(['realgdp', 'infl', 'unemp'], name='item'),index=periods.to_timestamp('D', 'end'))
print(data)
item realgdp infl unemp
date
1959-03-31 2710.349 0.00 5.8
1959-06-30 2778.801 2.34 5.1
1959-09-30 2775.488 2.74 5.3
1959-12-31 2785.204 0.27 5.6
1960-03-31 2847.699 2.31 5.2
1960-06-30 2834.390 0.14 5.2
1960-09-30 2839.022 2.70 5.6
1960-12-31 2802.616 1.21 6.3
1961-03-31 2819.264 -0.40 6.8
1961-06-30 2872.005 1.47 7.0
1961-09-30 2918.419 0.80 6.8
1961-12-31 2977.830 0.80 6.2
1962-03-31 3031.241 2.26 5.6
1962-06-30 3064.709 0.13 5.5
1962-09-30 3093.047 2.11 5.6
1962-12-31 3100.563 0.79 5.5
1963-03-31 3141.087 0.53 5.8
1963-06-30 3180.447 2.75 5.7
1963-09-30 3240.332 0.78 5.5
1963-12-31 3264.967 2.46 5.6
1964-03-31 3338.246 0.13 5.5
1964-06-30 3376.587 0.90 5.2
1964-09-30 3422.469 1.29 5.0
1964-12-31 3431.957 2.05 5.0
1965-03-31 3516.251 1.28 4.9
1965-06-30 3563.960 2.54 4.7
1965-09-30 3636.285 0.89 4.4
1965-12-31 3724.014 2.90 4.1
1966-03-31 3815.423 4.99 3.9
1966-06-30 3828.124 2.10 3.8
... ... ... ...
2002-06-30 11538.770 1.56 5.8
2002-09-30 11596.430 2.66 5.7
2002-12-31 11598.824 3.08 5.8
2003-03-31 11645.819 1.31 5.9
2003-06-30 11738.706 1.09 6.2
2003-09-30 11935.461 2.60 6.1
2003-12-31 12042.817 3.02 5.8
2004-03-31 12127.623 2.35 5.7
2004-06-30 12213.818 3.61 5.6
2004-09-30 12303.533 3.58 5.4
2004-12-31 12410.282 2.09 5.4
2005-03-31 12534.113 4.15 5.3
2005-06-30 12587.535 1.85 5.1
2005-09-30 12683.153 9.14 5.0
2005-12-31 12748.699 0.40 4.9
2006-03-31 12915.938 2.60 4.7
2006-06-30 12962.462 3.97 4.7
2006-09-30 12965.916 -1.58 4.7
2006-12-31 13060.679 3.30 4.4
2007-03-31 13099.901 4.58 4.5
2007-06-30 13203.977 2.75 4.5
2007-09-30 13321.109 3.45 4.7
2007-12-31 13391.249 6.38 4.8
2008-03-31 13366.865 2.82 4.9
2008-06-30 13415.266 8.53 5.4
2008-09-30 13324.600 -3.16 6.0
2008-12-31 13141.920 -8.79 6.9
2009-03-31 12925.410 0.94 8.1
2009-06-30 12901.504 3.37 9.2
2009-09-30 12990.341 3.56 9.6[203 rows x 3 columns]
ldata = data.stack().reset_index().rename(columns={0: 'value'})
print(ldata)
date item value
0 1959-03-31 realgdp 2710.349
1 1959-03-31 infl 0.000
2 1959-03-31 unemp 5.800
3 1959-06-30 realgdp 2778.801
4 1959-06-30 infl 2.340
5 1959-06-30 unemp 5.100
6 1959-09-30 realgdp 2775.488
7 1959-09-30 infl 2.740
8 1959-09-30 unemp 5.300
9 1959-12-31 realgdp 2785.204
10 1959-12-31 infl 0.270
11 1959-12-31 unemp 5.600
12 1960-03-31 realgdp 2847.699
13 1960-03-31 infl 2.310
14 1960-03-31 unemp 5.200
15 1960-06-30 realgdp 2834.390
16 1960-06-30 infl 0.140
17 1960-06-30 unemp 5.200
18 1960-09-30 realgdp 2839.022
19 1960-09-30 infl 2.700
20 1960-09-30 unemp 5.600
21 1960-12-31 realgdp 2802.616
22 1960-12-31 infl 1.210
23 1960-12-31 unemp 6.300
24 1961-03-31 realgdp 2819.264
25 1961-03-31 infl -0.400
26 1961-03-31 unemp 6.800
27 1961-06-30 realgdp 2872.005
28 1961-06-30 infl 1.470
29 1961-06-30 unemp 7.000
.. ... ... ...
579 2007-06-30 realgdp 13203.977
580 2007-06-30 infl 2.750
581 2007-06-30 unemp 4.500
582 2007-09-30 realgdp 13321.109
583 2007-09-30 infl 3.450
584 2007-09-30 unemp 4.700
585 2007-12-31 realgdp 13391.249
586 2007-12-31 infl 6.380
587 2007-12-31 unemp 4.800
588 2008-03-31 realgdp 13366.865
589 2008-03-31 infl 2.820
590 2008-03-31 unemp 4.900
591 2008-06-30 realgdp 13415.266
592 2008-06-30 infl 8.530
593 2008-06-30 unemp 5.400
594 2008-09-30 realgdp 13324.600
595 2008-09-30 infl -3.160
596 2008-09-30 unemp 6.000
597 2008-12-31 realgdp 13141.920
598 2008-12-31 infl -8.790
599 2008-12-31 unemp 6.900
600 2009-03-31 realgdp 12925.410
601 2009-03-31 infl 0.940
602 2009-03-31 unemp 8.100
603 2009-06-30 realgdp 12901.504
604 2009-06-30 infl 3.370
605 2009-06-30 unemp 9.200
606 2009-09-30 realgdp 12990.341
607 2009-09-30 infl 3.560
608 2009-09-30 unemp 9.600[609 rows x 3 columns]
wdata = ldata.pivot(index='date',columns= 'item',values= 'value')
print(wdata)
item infl realgdp unemp
date
1959-03-31 0.00 2710.349 5.8
1959-06-30 2.34 2778.801 5.1
1959-09-30 2.74 2775.488 5.3
1959-12-31 0.27 2785.204 5.6
1960-03-31 2.31 2847.699 5.2
1960-06-30 0.14 2834.390 5.2
1960-09-30 2.70 2839.022 5.6
1960-12-31 1.21 2802.616 6.3
1961-03-31 -0.40 2819.264 6.8
1961-06-30 1.47 2872.005 7.0
1961-09-30 0.80 2918.419 6.8
1961-12-31 0.80 2977.830 6.2
1962-03-31 2.26 3031.241 5.6
1962-06-30 0.13 3064.709 5.5
1962-09-30 2.11 3093.047 5.6
1962-12-31 0.79 3100.563 5.5
1963-03-31 0.53 3141.087 5.8
1963-06-30 2.75 3180.447 5.7
1963-09-30 0.78 3240.332 5.5
1963-12-31 2.46 3264.967 5.6
1964-03-31 0.13 3338.246 5.5
1964-06-30 0.90 3376.587 5.2
1964-09-30 1.29 3422.469 5.0
1964-12-31 2.05 3431.957 5.0
1965-03-31 1.28 3516.251 4.9
1965-06-30 2.54 3563.960 4.7
1965-09-30 0.89 3636.285 4.4
1965-12-31 2.90 3724.014 4.1
1966-03-31 4.99 3815.423 3.9
1966-06-30 2.10 3828.124 3.8
... ... ... ...
2002-06-30 1.56 11538.770 5.8
2002-09-30 2.66 11596.430 5.7
2002-12-31 3.08 11598.824 5.8
2003-03-31 1.31 11645.819 5.9
2003-06-30 1.09 11738.706 6.2
2003-09-30 2.60 11935.461 6.1
2003-12-31 3.02 12042.817 5.8
2004-03-31 2.35 12127.623 5.7
2004-06-30 3.61 12213.818 5.6
2004-09-30 3.58 12303.533 5.4
2004-12-31 2.09 12410.282 5.4
2005-03-31 4.15 12534.113 5.3
2005-06-30 1.85 12587.535 5.1
2005-09-30 9.14 12683.153 5.0
2005-12-31 0.40 12748.699 4.9
2006-03-31 2.60 12915.938 4.7
2006-06-30 3.97 12962.462 4.7
2006-09-30 -1.58 12965.916 4.7
2006-12-31 3.30 13060.679 4.4
2007-03-31 4.58 13099.901 4.5
2007-06-30 2.75 13203.977 4.5
2007-09-30 3.45 13321.109 4.7
2007-12-31 6.38 13391.249 4.8
2008-03-31 2.82 13366.865 4.9
2008-06-30 8.53 13415.266 5.4
2008-09-30 -3.16 13324.600 6.0
2008-12-31 -8.79 13141.920 6.9
2009-03-31 0.94 12925.410 8.1
2009-06-30 3.37 12901.504 9.2
2009-09-30 3.56 12990.341 9.6[203 rows x 3 columns]
# 2
ldata[:10]
|
date
|
item
|
value
|
|
0
|
1959-03-31
|
realgdp
|
2710.349
|
|
1
|
1959-03-31
|
infl
|
0.000
|
|
2
|
1959-03-31
|
unemp
|
5.800
|
|
3
|
1959-06-30
|
realgdp
|
2778.801
|
|
4
|
1959-06-30
|
infl
|
2.340
|
|
5
|
1959-06-30
|
unemp
|
5.100
|
|
6
|
1959-09-30
|
realgdp
|
2775.488
|
|
7
|
1959-09-30
|
infl
|
2.740
|
|
8
|
1959-09-30
|
unemp
|
5.300
|
|
9
|
1959-12-31
|
realgdp
|
2785.204
|
pivoted = ldata.pivot('date', 'item', 'value')
pivoted.head()
|
item
|
infl
|
realgdp
|
unemp
|
|
date
|
|
|
|
|
1959-03-31
|
0.00
|
2710.349
|
5.8
|
|
1959-06-30
|
2.34
|
2778.801
|
5.1
|
|
1959-09-30
|
2.74
|
2775.488
|
5.3
|
|
1959-12-31
|
0.27
|
2785.204
|
5.6
|
|
1960-03-31
|
2.31
|
2847.699
|
5.2
|
ldata['value2'] = np.random.randn(len(ldata))
ldata[:10]
|
date
|
item
|
value
|
value2
|
|
0
|
1959-03-31
|
realgdp
|
2710.349
|
1.669025
|
|
1
|
1959-03-31
|
infl
|
0.000
|
-0.438570
|
|
2
|
1959-03-31
|
unemp
|
5.800
|
-0.539741
|
|
3
|
1959-06-30
|
realgdp
|
2778.801
|
0.476985
|
|
4
|
1959-06-30
|
infl
|
2.340
|
3.248944
|
|
5
|
1959-06-30
|
unemp
|
5.100
|
-1.021228
|
|
6
|
1959-09-30
|
realgdp
|
2775.488
|
-0.577087
|
|
7
|
1959-09-30
|
infl
|
2.740
|
0.124121
|
|
8
|
1959-09-30
|
unemp
|
5.300
|
0.302614
|
|
9
|
1959-12-31
|
realgdp
|
2785.204
|
0.523772
|
pivoted = ldata.pivot('date', 'item')
pivoted[:5]
|
value
|
value2
|
|
item
|
infl
|
realgdp
|
unemp
|
infl
|
realgdp
|
unemp
|
|
date
|
|
|
|
|
|
|
|
1959-03-31
|
0.00
|
2710.349
|
5.8
|
-0.438570
|
1.669025
|
-0.539741
|
|
1959-06-30
|
2.34
|
2778.801
|
5.1
|
3.248944
|
0.476985
|
-1.021228
|
|
1959-09-30
|
2.74
|
2775.488
|
5.3
|
0.124121
|
-0.577087
|
0.302614
|
|
1959-12-31
|
0.27
|
2785.204
|
5.6
|
0.000940
|
0.523772
|
1.343810
|
|
1960-03-31
|
2.31
|
2847.699
|
5.2
|
-0.831154
|
-0.713544
|
-2.370232
|
pivoted['value'][:5]
|
item
|
infl
|
realgdp
|
unemp
|
|
date
|
|
|
|
|
1959-03-31
|
0.00
|
2710.349
|
5.8
|
|
1959-06-30
|
2.34
|
2778.801
|
5.1
|
|
1959-09-30
|
2.74
|
2775.488
|
5.3
|
|
1959-12-31
|
0.27
|
2785.204
|
5.6
|
|
1960-03-31
|
2.31
|
2847.699
|
5.2
|
unstacked = ldata.set_index(['date', 'item']).unstack('item')
unstacked[:7]
|
value
|
value2
|
|
item
|
infl
|
realgdp
|
unemp
|
infl
|
realgdp
|
unemp
|
|
date
|
|
|
|
|
|
|
|
1959-03-31
|
0.00
|
2710.349
|
5.8
|
-0.438570
|
1.669025
|
-0.539741
|
|
1959-06-30
|
2.34
|
2778.801
|
5.1
|
3.248944
|
0.476985
|
-1.021228
|
|
1959-09-30
|
2.74
|
2775.488
|
5.3
|
0.124121
|
-0.577087
|
0.302614
|
|
1959-12-31
|
0.27
|
2785.204
|
5.6
|
0.000940
|
0.523772
|
1.343810
|
|
1960-03-31
|
2.31
|
2847.699
|
5.2
|
-0.831154
|
-0.713544
|
-2.370232
|
|
1960-06-30
|
0.14
|
2834.390
|
5.2
|
-0.860757
|
-1.860761
|
0.560145
|
|
1960-09-30
|
2.70
|
2839.022
|
5.6
|
0.119827
|
-1.265934
|
-1.063512
|
移除重复数据
data = DataFrame({'k1': ['one'] * 3 + ['two'] * 4,'k2': [1, 1, 2, 3, 3, 4, 4]})
print(data)
k1 k2
0 one 1
1 one 1
2 one 2
3 two 3
4 two 3
5 two 4
6 two 4
data.duplicated()
0 False
1 True
2 False
3 False
4 True
5 False
6 True
dtype: bool
data.drop_duplicates()
|
k1
|
k2
|
|
0
|
one
|
1
|
|
2
|
one
|
2
|
|
3
|
two
|
3
|
|
5
|
two
|
4
|
data['v1'] = range(7)
print(data)
k1 k2 v1
0 one 1 0
1 one 1 1
2 one 2 2
3 two 3 3
4 two 3 4
5 two 4 5
6 two 4 6
data.drop_duplicates(['k1'])
|
k1
|
k2
|
v1
|
|
0
|
one
|
1
|
0
|
|
3
|
two
|
3
|
3
|
data.drop_duplicates(['k1', 'k2'], keep='last')
|
k1
|
k2
|
v1
|
|
1
|
one
|
1
|
1
|
|
2
|
one
|
2
|
2
|
|
4
|
two
|
3
|
4
|
|
6
|
two
|
4
|
6
|
利用函数或映射进行数据转换
# 1
data = DataFrame({'food': ['bacon', 'pulled pork', 'bacon', 'Pastrami','corned beef', 'Bacon', 'pastrami', 'honey ham','nova lox'],'ounces': [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})
print(data)
food ounces
0 bacon 4.0
1 pulled pork 3.0
2 bacon 12.0
3 Pastrami 6.0
4 corned beef 7.5
5 Bacon 8.0
6 pastrami 3.0
7 honey ham 5.0
8 nova lox 6.0
meat_to_animal = {'bacon': 'pig','pulled pork': 'pig','pastrami': 'cow','corned beef': 'cow','honey ham': 'pig','nova lox': 'salmon'
}data['animal'] = data['food'].map(str.lower).map(meat_to_animal)
print(data)
food ounces animal
0 bacon 4.0 pig
1 pulled pork 3.0 pig
2 bacon 12.0 pig
3 Pastrami 6.0 cow
4 corned beef 7.5 cow
5 Bacon 8.0 pig
6 pastrami 3.0 cow
7 honey ham 5.0 pig
8 nova lox 6.0 salmon
data['food'].map(lambda x: meat_to_animal[x.lower()])
0 pig
1 pig
2 pig
3 cow
4 cow
5 pig
6 cow
7 pig
8 salmon
Name: food, dtype: object
数据标准化
datafile = 'data/normalization_data.xls' # 参数初始化
data = pd.read_excel(datafile, header=None) # 读取数据
print(data)
0 1 2 3
0 78 521 602 2863
1 144 -600 -521 2245
2 95 -457 468 -1283
3 69 596 695 1054
4 190 527 691 2051
5 101 403 470 2487
6 146 413 435 2571
最小-最大规范化
(data - data.min()) / (data.max() - data.min()) # 最小-最大规范化
|
0
|
1
|
2
|
3
|
|
0
|
0.074380
|
0.937291
|
0.923520
|
1.000000
|
|
1
|
0.619835
|
0.000000
|
0.000000
|
0.850941
|
|
2
|
0.214876
|
0.119565
|
0.813322
|
0.000000
|
|
3
|
0.000000
|
1.000000
|
1.000000
|
0.563676
|
|
4
|
1.000000
|
0.942308
|
0.996711
|
0.804149
|
|
5
|
0.264463
|
0.838629
|
0.814967
|
0.909310
|
|
6
|
0.636364
|
0.846990
|
0.786184
|
0.929571
|
零-均值规范化
(data - data.mean()) / data.std() # 零-均值规范化
|
0
|
1
|
2
|
3
|
|
0
|
-0.905383
|
0.635863
|
0.464531
|
0.798149
|
|
1
|
0.604678
|
-1.587675
|
-2.193167
|
0.369390
|
|
2
|
-0.516428
|
-1.304030
|
0.147406
|
-2.078279
|
|
3
|
-1.111301
|
0.784628
|
0.684625
|
-0.456906
|
|
4
|
1.657146
|
0.647765
|
0.675159
|
0.234796
|
|
5
|
-0.379150
|
0.401807
|
0.152139
|
0.537286
|
|
6
|
0.650438
|
0.421642
|
0.069308
|
0.595564
|
小数定标规范化
data / 10 ** np.ceil(np.log10(data.abs().max())) # 小数定标规范化
|
0
|
1
|
2
|
3
|
|
0
|
0.078
|
0.521
|
0.602
|
0.2863
|
|
1
|
0.144
|
-0.600
|
-0.521
|
0.2245
|
|
2
|
0.095
|
-0.457
|
0.468
|
-0.1283
|
|
3
|
0.069
|
0.596
|
0.695
|
0.1054
|
|
4
|
0.190
|
0.527
|
0.691
|
0.2051
|
|
5
|
0.101
|
0.403
|
0.470
|
0.2487
|
|
6
|
0.146
|
0.413
|
0.435
|
0.2571
|
替换值
data = Series([1., -999., 2., -999., -1000., 3.])
print(data)
0 1.0
1 -999.0
2 2.0
3 -999.0
4 -1000.0
5 3.0
dtype: float64
data.replace(-999, np.nan)
0 1.0
1 NaN
2 2.0
3 NaN
4 -1000.0
5 3.0
dtype: float64
data.replace([-999, -1000], np.nan)
0 1.0
1 NaN
2 2.0
3 NaN
4 NaN
5 3.0
dtype: float64
data.replace([-999, -1000], [np.nan, 0])
0 1.0
1 NaN
2 2.0
3 NaN
4 0.0
5 3.0
dtype: float64
data.replace({-999: np.nan, -1000: 0})
0 1.0
1 NaN
2 2.0
3 NaN
4 0.0
5 3.0
dtype: float64
重命名轴索引
data = DataFrame(np.arange(12).reshape((3, 4)),index=['Ohio', 'Colorado', 'New York'],columns=['one', 'two', 'three', 'four'])
print(data)
one two three four
Ohio 0 1 2 3
Colorado 4 5 6 7
New York 8 9 10 11
data.index.map(str.upper)
Index(['OHIO', 'COLORADO', 'NEW YORK'], dtype='object')
data.index = data.index.map(str.upper)
print(data)
one two three four
OHIO 0 1 2 3
COLORADO 4 5 6 7
NEW YORK 8 9 10 11
data.rename(index=str.title, columns=str.upper)
|
ONE
|
TWO
|
THREE
|
FOUR
|
|
Ohio
|
0
|
1
|
2
|
3
|
|
Colorado
|
4
|
5
|
6
|
7
|
|
New York
|
8
|
9
|
10
|
11
|
data.rename(index={'OHIO': 'INDIANA'},columns={'three': 'peekaboo'})
|
one
|
two
|
peekaboo
|
four
|
|
INDIANA
|
0
|
1
|
2
|
3
|
|
COLORADO
|
4
|
5
|
6
|
7
|
|
NEW YORK
|
8
|
9
|
10
|
11
|
# 总是返回DataFrame的引用
data.rename(index={'OHIO': 'INDIANA'}, inplace=True)
print(data)
one two three four
INDIANA 0 1 2 3
COLORADO 4 5 6 7
NEW YORK 8 9 10 11
离散化与面元划分
# 1
ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32]bins = [18, 25, 35, 60, 100]
cats = pd.cut(ages, bins)
print(cats)
[(18, 25], (18, 25], (18, 25], (25, 35], (18, 25], ..., (25, 35], (60, 100], (35, 60], (35, 60], (25, 35]]
Length: 12
Categories (4, interval[int64]): [(18, 25] < (25, 35] < (35, 60] < (60, 100]]
cats.codes
array([0, 0, 0, 1, 0, 0, 2, 1, 3, 2, 2, 1], dtype=int8)
cats.categories
IntervalIndex([(18, 25], (25, 35], (35, 60], (60, 100]]closed='right',dtype='interval[int64]')
pd.value_counts(cats)
(18, 25] 5
(35, 60] 3
(25, 35] 3
(60, 100] 1
dtype: int64
pd.cut(ages, [18, 26, 36, 61, 100], right=False)
[[18, 26), [18, 26), [18, 26), [26, 36), [18, 26), ..., [26, 36), [61, 100), [36, 61), [36, 61), [26, 36)]
Length: 12
Categories (4, interval[int64]): [[18, 26) < [26, 36) < [36, 61) < [61, 100)]
group_names = ['Youth', 'YoungAdult', 'MiddleAged', 'Senior']
pd.cut(ages, bins, labels=group_names)
[Youth, Youth, Youth, YoungAdult, Youth, ..., YoungAdult, Senior, MiddleAged, MiddleAged, YoungAdult]
Length: 12
Categories (4, object): [MiddleAged < Senior < YoungAdult < Youth]
data = np.random.rand(20)
pd.cut(data, 4, precision=2)
[(0.45, 0.67], (0.23, 0.45], (0.0037, 0.23], (0.45, 0.67], (0.67, 0.9], ..., (0.67, 0.9], (0.0037, 0.23], (0.0037, 0.23], (0.23, 0.45], (0.23, 0.45]]
Length: 20
Categories (4, interval[float64]): [(0.0037, 0.23] < (0.23, 0.45] < (0.45, 0.67] < (0.67, 0.9]]
# 2
data = np.random.randn(1000) # Normally distributed
cats = pd.qcut(data, 4) # Cut into quartiles
print(cats)
[(-0.022, 0.641], (-3.746, -0.635], (0.641, 3.26], (-3.746, -0.635], (-0.022, 0.641], ..., (-0.022, 0.641], (0.641, 3.26], (-0.635, -0.022], (0.641, 3.26], (-0.635, -0.022]]
Length: 1000
Categories (4, interval[float64]): [(-3.746, -0.635] < (-0.635, -0.022] < (-0.022, 0.641] < (0.641, 3.26]]
pd.value_counts(cats)
(0.641, 3.26] 250
(-0.022, 0.641] 250
(-0.635, -0.022] 250
(-3.746, -0.635] 250
dtype: int64
pd.qcut(data, [0, 0.1, 0.5, 0.9, 1.])
[(-0.022, 1.302], (-1.266, -0.022], (-0.022, 1.302], (-3.746, -1.266], (-0.022, 1.302], ..., (-0.022, 1.302], (-0.022, 1.302], (-1.266, -0.022], (-0.022, 1.302], (-1.266, -0.022]]
Length: 1000
Categories (4, interval[float64]): [(-3.746, -1.266] < (-1.266, -0.022] < (-0.022, 1.302] < (1.302, 3.26]]
检测和过滤异常值
# 1
np.random.seed(12345)
data = DataFrame(np.random.randn(1000, 4))
data.describe()
|
0
|
1
|
2
|
3
|
|
count
|
1000.000000
|
1000.000000
|
1000.000000
|
1000.000000
|
|
mean
|
-0.067684
|
0.067924
|
0.025598
|
-0.002298
|
|
std
|
0.998035
|
0.992106
|
1.006835
|
0.996794
|
|
min
|
-3.428254
|
-3.548824
|
-3.184377
|
-3.745356
|
|
25%
|
-0.774890
|
-0.591841
|
-0.641675
|
-0.644144
|
|
50%
|
-0.116401
|
0.101143
|
0.002073
|
-0.013611
|
|
75%
|
0.616366
|
0.780282
|
0.680391
|
0.654328
|
|
max
|
3.366626
|
2.653656
|
3.260383
|
3.927528
|
col = data[3]
col[np.abs(col) > 3]
97 3.927528
305 -3.399312
400 -3.745356
Name: 3, dtype: float64
data[(np.abs(data) > 3).any(1)]
|
0
|
1
|
2
|
3
|
|
5
|
-0.539741
|
0.476985
|
3.248944
|
-1.021228
|
|
97
|
-0.774363
|
0.552936
|
0.106061
|
3.927528
|
|
102
|
-0.655054
|
-0.565230
|
3.176873
|
0.959533
|
|
305
|
-2.315555
|
0.457246
|
-0.025907
|
-3.399312
|
|
324
|
0.050188
|
1.951312
|
3.260383
|
0.963301
|
|
400
|
0.146326
|
0.508391
|
-0.196713
|
-3.745356
|
|
499
|
-0.293333
|
-0.242459
|
-3.056990
|
1.918403
|
|
523
|
-3.428254
|
-0.296336
|
-0.439938
|
-0.867165
|
|
586
|
0.275144
|
1.179227
|
-3.184377
|
1.369891
|
|
808
|
-0.362528
|
-3.548824
|
1.553205
|
-2.186301
|
|
900
|
3.366626
|
-2.372214
|
0.851010
|
1.332846
|
# 2
data[np.abs(data) > 3] = np.sign(data) * 3
data.describe()
|
0
|
1
|
2
|
3
|
|
count
|
1000.000000
|
1000.000000
|
1000.000000
|
1000.000000
|
|
mean
|
-0.067623
|
0.068473
|
0.025153
|
-0.002081
|
|
std
|
0.995485
|
0.990253
|
1.003977
|
0.989736
|
|
min
|
-3.000000
|
-3.000000
|
-3.000000
|
-3.000000
|
|
25%
|
-0.774890
|
-0.591841
|
-0.641675
|
-0.644144
|
|
50%
|
-0.116401
|
0.101143
|
0.002073
|
-0.013611
|
|
75%
|
0.616366
|
0.780282
|
0.680391
|
0.654328
|
|
max
|
3.000000
|
2.653656
|
3.000000
|
3.000000
|
排列与随机采样
df = DataFrame(np.arange(5 * 4).reshape((5, 4)))
print(df)
0 1 2 3
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
3 12 13 14 15
4 16 17 18 19
sampler = np.random.permutation(5)
print(sampler)
[1 0 2 3 4]
df.take(sampler)
|
0
|
1
|
2
|
3
|
|
1
|
4
|
5
|
6
|
7
|
|
0
|
0
|
1
|
2
|
3
|
|
2
|
8
|
9
|
10
|
11
|
|
3
|
12
|
13
|
14
|
15
|
|
4
|
16
|
17
|
18
|
19
|
# 2
df.take(np.random.permutation(len(df))[:3])
|
0
|
1
|
2
|
3
|
|
1
|
4
|
5
|
6
|
7
|
|
3
|
12
|
13
|
14
|
15
|
|
4
|
16
|
17
|
18
|
19
|
# 3
bag = np.array([5, 7, -1, 6, 4])
sampler = np.random.randint(0, len(bag), size=10)
print(sampler)
[4 4 2 2 2 0 3 0 4 1]
draws = bag.take(sampler)
print(draws)
[ 4 4 -1 -1 -1 5 6 5 4 7]
计算指标与哑变量
# 1
df = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'b'],'data1': range(6)})
print(df)
data1 key
0 0 b
1 1 b
2 2 a
3 3 c
4 4 a
5 5 b
pd.get_dummies(df['key'])
|
a
|
b
|
c
|
|
0
|
0
|
1
|
0
|
|
1
|
0
|
1
|
0
|
|
2
|
1
|
0
|
0
|
|
3
|
0
|
0
|
1
|
|
4
|
1
|
0
|
0
|
|
5
|
0
|
1
|
0
|
dummies = pd.get_dummies(df['key'], prefix='key')
print(dummies)
key_a key_b key_c
0 0 1 0
1 0 1 0
2 1 0 0
3 0 0 1
4 1 0 0
5 0 1 0
df_with_dummy = df[['data1']].join(dummies)
print(df_with_dummy)
data1 key_a key_b key_c
0 0 0 1 0
1 1 0 1 0
2 2 1 0 0
3 3 0 0 1
4 4 1 0 0
5 5 0 1 0
# 2
mnames = ['movie_id', 'title', 'genres']
movies = pd.read_table('data/movies.dat', sep='::', header=None,names=mnames)
movies[:10]
C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:4: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.after removing the cwd from sys.path.
|
movie_id
|
title
|
genres
|
|
0
|
1
|
Toy Story (1995)
|
Animation|Children's|Comedy
|
|
1
|
2
|
Jumanji (1995)
|
Adventure|Children's|Fantasy
|
|
2
|
3
|
Grumpier Old Men (1995)
|
Comedy|Romance
|
|
3
|
4
|
Waiting to Exhale (1995)
|
Comedy|Drama
|
|
4
|
5
|
Father of the Bride Part II (1995)
|
Comedy
|
|
5
|
6
|
Heat (1995)
|
Action|Crime|Thriller
|
|
6
|
7
|
Sabrina (1995)
|
Comedy|Romance
|
|
7
|
8
|
Tom and Huck (1995)
|
Adventure|Children's
|
|
8
|
9
|
Sudden Death (1995)
|
Action
|
|
9
|
10
|
GoldenEye (1995)
|
Action|Adventure|Thriller
|
genre_iter = (set(x.split('|')) for x in movies.genres)
genres = sorted(set.union(*genre_iter))
print(genres)
['Action', 'Adventure', 'Animation', "Children's", 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy', 'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western']
dummies = DataFrame(np.zeros((len(movies), len(genres))), columns=genres)
print(dummies)
Action Adventure Animation Children's Comedy Crime Documentary \
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0
5 0.0 0.0 0.0 0.0 0.0 0.0 0.0
6 0.0 0.0 0.0 0.0 0.0 0.0 0.0
7 0.0 0.0 0.0 0.0 0.0 0.0 0.0
8 0.0 0.0 0.0 0.0 0.0 0.0 0.0
9 0.0 0.0 0.0 0.0 0.0 0.0 0.0
10 0.0 0.0 0.0 0.0 0.0 0.0 0.0
11 0.0 0.0 0.0 0.0 0.0 0.0 0.0
12 0.0 0.0 0.0 0.0 0.0 0.0 0.0
13 0.0 0.0 0.0 0.0 0.0 0.0 0.0
14 0.0 0.0 0.0 0.0 0.0 0.0 0.0
15 0.0 0.0 0.0 0.0 0.0 0.0 0.0
16 0.0 0.0 0.0 0.0 0.0 0.0 0.0
17 0.0 0.0 0.0 0.0 0.0 0.0 0.0
18 0.0 0.0 0.0 0.0 0.0 0.0 0.0
19 0.0 0.0 0.0 0.0 0.0 0.0 0.0
20 0.0 0.0 0.0 0.0 0.0 0.0 0.0
21 0.0 0.0 0.0 0.0 0.0 0.0 0.0
22 0.0 0.0 0.0 0.0 0.0 0.0 0.0
23 0.0 0.0 0.0 0.0 0.0 0.0 0.0
24 0.0 0.0 0.0 0.0 0.0 0.0 0.0
25 0.0 0.0 0.0 0.0 0.0 0.0 0.0
26 0.0 0.0 0.0 0.0 0.0 0.0 0.0
27 0.0 0.0 0.0 0.0 0.0 0.0 0.0
28 0.0 0.0 0.0 0.0 0.0 0.0 0.0
29 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ...
3853 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3854 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3855 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3856 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3857 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3858 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3859 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3860 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3861 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3862 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3863 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3864 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3865 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3866 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3867 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3868 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3869 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3870 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3871 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3872 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3873 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3874 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3875 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3876 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3877 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3878 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3879 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3880 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3881 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3882 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Drama Fantasy Film-Noir Horror Musical Mystery Romance Sci-Fi \
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
24 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
25 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
26 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
27 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
28 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
29 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ...
3853 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3854 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3855 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3856 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3857 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3858 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3859 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3860 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3861 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3862 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3863 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3864 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3865 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3866 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3867 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3868 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3869 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3870 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3871 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3872 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3873 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3874 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3875 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3876 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3877 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3878 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3879 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3880 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3881 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3882 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Thriller War Western
0 0.0 0.0 0.0
1 0.0 0.0 0.0
2 0.0 0.0 0.0
3 0.0 0.0 0.0
4 0.0 0.0 0.0
5 0.0 0.0 0.0
6 0.0 0.0 0.0
7 0.0 0.0 0.0
8 0.0 0.0 0.0
9 0.0 0.0 0.0
10 0.0 0.0 0.0
11 0.0 0.0 0.0
12 0.0 0.0 0.0
13 0.0 0.0 0.0
14 0.0 0.0 0.0
15 0.0 0.0 0.0
16 0.0 0.0 0.0
17 0.0 0.0 0.0
18 0.0 0.0 0.0
19 0.0 0.0 0.0
20 0.0 0.0 0.0
21 0.0 0.0 0.0
22 0.0 0.0 0.0
23 0.0 0.0 0.0
24 0.0 0.0 0.0
25 0.0 0.0 0.0
26 0.0 0.0 0.0
27 0.0 0.0 0.0
28 0.0 0.0 0.0
29 0.0 0.0 0.0
... ... ... ...
3853 0.0 0.0 0.0
3854 0.0 0.0 0.0
3855 0.0 0.0 0.0
3856 0.0 0.0 0.0
3857 0.0 0.0 0.0
3858 0.0 0.0 0.0
3859 0.0 0.0 0.0
3860 0.0 0.0 0.0
3861 0.0 0.0 0.0
3862 0.0 0.0 0.0
3863 0.0 0.0 0.0
3864 0.0 0.0 0.0
3865 0.0 0.0 0.0
3866 0.0 0.0 0.0
3867 0.0 0.0 0.0
3868 0.0 0.0 0.0
3869 0.0 0.0 0.0
3870 0.0 0.0 0.0
3871 0.0 0.0 0.0
3872 0.0 0.0 0.0
3873 0.0 0.0 0.0
3874 0.0 0.0 0.0
3875 0.0 0.0 0.0
3876 0.0 0.0 0.0
3877 0.0 0.0 0.0
3878 0.0 0.0 0.0
3879 0.0 0.0 0.0
3880 0.0 0.0 0.0
3881 0.0 0.0 0.0
3882 0.0 0.0 0.0 [3883 rows x 18 columns]
for i, gen in enumerate(movies.genres):dummies.loc[i, gen.split('|')] = 1movies_windic = movies.join(dummies.add_prefix('Genre_'))
print(movies_windic.loc[0])
movie_id 1
title Toy Story (1995)
genres Animation|Children's|Comedy
Genre_Action 0
Genre_Adventure 0
Genre_Animation 1
Genre_Children's 1
Genre_Comedy 1
Genre_Crime 0
Genre_Documentary 0
Genre_Drama 0
Genre_Fantasy 0
Genre_Film-Noir 0
Genre_Horror 0
Genre_Musical 0
Genre_Mystery 0
Genre_Romance 0
Genre_Sci-Fi 0
Genre_Thriller 0
Genre_War 0
Genre_Western 0
Name: 0, dtype: object
# 3
np.random.seed(12345)
values = np.random.rand(10)
print(values)
[ 0.92961609 0.31637555 0.18391881 0.20456028 0.56772503 0.59554470.96451452 0.6531771 0.74890664 0.65356987]
bins = [0, 0.2, 0.4, 0.6, 0.8, 1]
pd.get_dummies(pd.cut(values, bins))
|
(0.0, 0.2]
|
(0.2, 0.4]
|
(0.4, 0.6]
|
(0.6, 0.8]
|
(0.8, 1.0]
|
|
0
|
0
|
0
|
0
|
0
|
1
|
|
1
|
0
|
1
|
0
|
0
|
0
|
|
2
|
1
|
0
|
0
|
0
|
0
|
|
3
|
0
|
1
|
0
|
0
|
0
|
|
4
|
0
|
0
|
1
|
0
|
0
|
|
5
|
0
|
0
|
1
|
0
|
0
|
|
6
|
0
|
0
|
0
|
0
|
1
|
|
7
|
0
|
0
|
0
|
1
|
0
|
|
8
|
0
|
0
|
0
|
1
|
0
|
|
9
|
0
|
0
|
0
|
1
|
0
|
属性构造
# 参数初始化
inputfile = 'data/electricity_data.xls' # 供入供出电量数据
outputfile = 'data/electricity_data.xls' # 属性构造后数据文件data = pd.read_excel(inputfile) # 读入数据
data[u'线损率'] = (data[u'供入电量'] - data[u'供出电量']) / data[u'供入电量']data.to_excel(outputfile, index=False) # 保存结果
字符串对象方法
val = 'a,b, guido'
val.split(',')
['a', 'b', ' guido']
pieces = [x.strip() for x in val.split(',')]
print(pieces)
['a', 'b', 'guido']
first, second, third = pieces
first + '::' + second + '::' + third
'a::b::guido'
'::'.join(pieces)
'a::b::guido'
'guido' in val
True
val.index(',')
1
val.find(':')
-1
val.index(':')
---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-138-280f8b2856ce> in <module>()
----> 1 val.index(':')ValueError: substring not found
val.count('a')
1
val.replace(',', '::')
'a::b:: guido'
val.replace(',', '')
'ab guido'
正则表达式
# 1
import retext = "foo bar\t baz \tqux"
re.split('\s+', text)
['foo', 'bar', 'baz', 'qux']
regex = re.compile('\s+')
regex.split(text)
['foo', 'bar', 'baz', 'qux']
regex.findall(text)
[' ', '\t ', ' \t']
# 2
text = """Dave dave@google.com
Steve steve@gmail.com
Rob rob@gmail.com
Ryan ryan@yahoo.com
"""
pattern = r'[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,4}'# re.IGNORECASE 的作用是使正则表达式对大小写不敏感
regex = re.compile(pattern, flags=re.IGNORECASE)regex.findall(text)
['dave@google.com', 'steve@gmail.com', 'rob@gmail.com', 'ryan@yahoo.com']
m = regex.search(text)
print(m)
<_sre.SRE_Match object; span=(5, 20), match='dave@google.com'>
text[m.start():m.end()]
'dave@google.com'
print(regex.match(text))
None
print(regex.sub('REDACTED', text))
Dave REDACTED
Steve REDACTED
Rob REDACTED
Ryan REDACTED
# 3
pattern = r'([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\.([A-Z]{2,4})'
regex = re.compile(pattern, flags=re.IGNORECASE)m = regex.match('wesm@bright.net')
m.groups()
('wesm', 'bright', 'net')
regex.findall(text)
[('dave', 'google', 'com'),('steve', 'gmail', 'com'),('rob', 'gmail', 'com'),('ryan', 'yahoo', 'com')]
print(regex.sub(r'Username: \1, Domain: \2, Suffix: \3', text))
Dave Username: dave, Domain: google, Suffix: com
Steve Username: steve, Domain: gmail, Suffix: com
Rob Username: rob, Domain: gmail, Suffix: com
Ryan Username: ryan, Domain: yahoo, Suffix: com
# 4
regex = re.compile(r"""(?P<username>[A-Z0-9._%+-]+)@(?P<domain>[A-Z0-9.-]+)\.(?P<suffix>[A-Z]{2,4})""", flags=re.IGNORECASE | re.VERBOSE)m = regex.match('wesm@bright.net')
m.groupdict()
{'domain': 'bright', 'suffix': 'net', 'username': 'wesm'}
pandas中矢量化的字符串函数
data = {'Dave': 'dave@google.com', 'Steve': 'steve@gmail.com','Rob': 'rob@gmail.com', 'Wes': np.nan}
data = Series(data)
print(data)
Dave dave@google.com
Rob rob@gmail.com
Steve steve@gmail.com
Wes NaN
dtype: object
data.isnull()
Dave False
Rob False
Steve False
Wes True
dtype: bool
data.str.contains('gmail')
Dave False
Rob True
Steve True
Wes NaN
dtype: object
data.str.findall(pattern, flags=re.IGNORECASE)
Dave [(dave, google, com)]
Rob [(rob, gmail, com)]
Steve [(steve, gmail, com)]
Wes NaN
dtype: object
matches = data.str.match(pattern, flags=re.IGNORECASE)
print(matches)
Dave True
Rob True
Steve True
Wes NaN
dtype: object
matches.str.get(1)
Dave NaN
Rob NaN
Steve NaN
Wes NaN
dtype: float64
matches.str[0]
Dave NaN
Rob NaN
Steve NaN
Wes NaN
dtype: float64
data.str[:5]
Dave dave@
Rob rob@g
Steve steve
Wes NaN
dtype: object
示例：USDA食品数据库
'''
{"id": 21441,"description": "KENTUCKY FRIED CHICKEN, Fried Chicken, EXTRA CRISPY,
Wing, meat and skin with breading","tags": ["KFC"],"manufacturer": "Kentucky Fried Chicken","group": "Fast Foods","portions": [{"amount": 1,"unit": "wing, with skin","grams": 68.0},...],"nutrients": [{"value": 20.8,"units": "g","description": "Protein","group": "Composition"},...]
}
'''import jsondb = json.load(open('data/foods-2011-10-03.json'))
len(db)
6636
db[0].keys()
dict_keys(['id', 'description', 'tags', 'manufacturer', 'group', 'portions', 'nutrients'])
db[0]['nutrients'][0]
{'description': 'Protein','group': 'Composition','units': 'g','value': 25.18}
nutrients = DataFrame(db[0]['nutrients'])
nutrients[:7]
|
description
|
group
|
units
|
value
|
|
0
|
Protein
|
Composition
|
g
|
25.18
|
|
1
|
Total lipid (fat)
|
Composition
|
g
|
29.20
|
|
2
|
Carbohydrate, by difference
|
Composition
|
g
|
3.06
|
|
3
|
Ash
|
Other
|
g
|
3.28
|
|
4
|
Energy
|
Energy
|
kcal
|
376.00
|
|
5
|
Water
|
Composition
|
g
|
39.28
|
|
6
|
Energy
|
Energy
|
kJ
|
1573.00
|
info_keys = ['description', 'group', 'id', 'manufacturer']
info = DataFrame(db, columns=info_keys)info[:5]
|
description
|
group
|
id
|
manufacturer
|
|
0
|
Cheese, caraway
|
Dairy and Egg Products
|
1008
|
|
|
1
|
Cheese, cheddar
|
Dairy and Egg Products
|
1009
|
|
|
2
|
Cheese, edam
|
Dairy and Egg Products
|
1018
|
|
|
3
|
Cheese, feta
|
Dairy and Egg Products
|
1019
|
|
|
4
|
Cheese, mozzarella, part skim milk
|
Dairy and Egg Products
|
1028
|
|
pd.value_counts(info.group)[:10]
Vegetables and Vegetable Products 812
Beef Products 618
Baked Products 496
Breakfast Cereals 403
Fast Foods 365
Legumes and Legume Products 365
Lamb, Veal, and Game Products 345
Sweets 341
Fruits and Fruit Juices 328
Pork Products 328
Name: group, dtype: int64
nutrients = []for rec in db:fnuts = DataFrame(rec['nutrients'])fnuts['id'] = rec['id']nutrients.append(fnuts)nutrients = pd.concat(nutrients, ignore_index=True)print(nutrients)
description group units value \
0 Protein Composition g 25.180
1 Total lipid (fat) Composition g 29.200
2 Carbohydrate, by difference Composition g 3.060
3 Ash Other g 3.280
4 Energy Energy kcal 376.000
5 Water Composition g 39.280
6 Energy Energy kJ 1573.000
7 Fiber, total dietary Composition g 0.000
8 Calcium, Ca Elements mg 673.000
9 Iron, Fe Elements mg 0.640
10 Magnesium, Mg Elements mg 22.000
11 Phosphorus, P Elements mg 490.000
12 Potassium, K Elements mg 93.000
13 Sodium, Na Elements mg 690.000
14 Zinc, Zn Elements mg 2.940
15 Copper, Cu Elements mg 0.024
16 Manganese, Mn Elements mg 0.021
17 Selenium, Se Elements mcg 14.500
18 Vitamin A, IU Vitamins IU 1054.000
19 Retinol Vitamins mcg 262.000
20 Vitamin A, RAE Vitamins mcg_RAE 271.000
21 Vitamin C, total ascorbic acid Vitamins mg 0.000
22 Thiamin Vitamins mg 0.031
23 Riboflavin Vitamins mg 0.450
24 Niacin Vitamins mg 0.180
25 Pantothenic acid Vitamins mg 0.190
26 Vitamin B-6 Vitamins mg 0.074
27 Folate, total Vitamins mcg 18.000
28 Vitamin B-12 Vitamins mcg 0.270
29 Folic acid Vitamins mcg 0.000
... ... ... ... ...
389325 Selenium, Se Elements mcg 1.100
389326 Vitamin A, IU Vitamins IU 5.000
389327 Retinol Vitamins mcg 0.000
389328 Vitamin A, RAE Vitamins mcg_RAE 0.000
389329 Carotene, beta Vitamins mcg 2.000
389330 Carotene, alpha Vitamins mcg 2.000
389331 Vitamin E (alpha-tocopherol) Vitamins mg 0.250
389332 Vitamin D Vitamins IU 0.000
389333 Vitamin D (D2 + D3) Vitamins mcg 0.000
389334 Cryptoxanthin, beta Vitamins mcg 0.000
389335 Lycopene Vitamins mcg 0.000
389336 Lutein + zeaxanthin Vitamins mcg 20.000
389337 Vitamin C, total ascorbic acid Vitamins mg 21.900
389338 Thiamin Vitamins mg 0.020
389339 Riboflavin Vitamins mg 0.060
389340 Niacin Vitamins mg 0.540
389341 Vitamin B-6 Vitamins mg 0.260
389342 Folate, total Vitamins mcg 17.000
389343 Vitamin B-12 Vitamins mcg 0.000
389344 Choline, total Vitamins mg 4.100
389345 Vitamin K (phylloquinone) Vitamins mcg 0.500
389346 Folic acid Vitamins mcg 0.000
389347 Folate, food Vitamins mcg 17.000
389348 Folate, DFE Vitamins mcg_DFE 17.000
389349 Vitamin E, added Vitamins mg 0.000
389350 Vitamin B-12, added Vitamins mcg 0.000
389351 Cholesterol Other mg 0.000
389352 Fatty acids, total saturated Other g 0.072
389353 Fatty acids, total monounsaturated Other g 0.028
389354 Fatty acids, total polyunsaturated Other g 0.041 id
0 1008
1 1008
2 1008
3 1008
4 1008
5 1008
6 1008
7 1008
8 1008
9 1008
10 1008
11 1008
12 1008
13 1008
14 1008
15 1008
16 1008
17 1008
18 1008
19 1008
20 1008
21 1008
22 1008
23 1008
24 1008
25 1008
26 1008
27 1008
28 1008
29 1008
... ...
389325 43546
389326 43546
389327 43546
389328 43546
389329 43546
389330 43546
389331 43546
389332 43546
389333 43546
389334 43546
389335 43546
389336 43546
389337 43546
389338 43546
389339 43546
389340 43546
389341 43546
389342 43546
389343 43546
389344 43546
389345 43546
389346 43546
389347 43546
389348 43546
389349 43546
389350 43546
389351 43546
389352 43546
389353 43546
389354 43546 [389355 rows x 5 columns]
# 数据中有重复的数据
nutrients.duplicated().sum()
14179
# 去重
nutrients = nutrients.drop_duplicates()
col_mapping = {'description': 'food','group': 'fgroup'}
info = info.rename(columns=col_mapping, copy=False)
print(info)
food \
0 Cheese, caraway
1 Cheese, cheddar
2 Cheese, edam
3 Cheese, feta
4 Cheese, mozzarella, part skim milk
5 Cheese, mozzarella, part skim milk, low moisture
6 Cheese, romano
7 Cheese, roquefort
8 Cheese spread, pasteurized process, american, ...
9 Cream, fluid, half and half
10 Sour dressing, non-butterfat, cultured, filled...
11 Milk, filled, fluid, with blend of hydrogenate...
12 Cream substitute, liquid, with lauric acid oil...
13 Cream substitute, powdered
14 Milk, producer, fluid, 3.7% milkfat
15 Milk, reduced fat, fluid, 2% milkfat, with add...
16 Milk, reduced fat, fluid, 2% milkfat, with add...
17 Milk, reduced fat, fluid, 2% milkfat, protein ...
18 Milk, lowfat, fluid, 1% milkfat, with added vi...
19 Milk, lowfat, fluid, 1% milkfat, with added no...
20 Milk, lowfat, fluid, 1% milkfat, protein forti...
21 Milk, nonfat, fluid, with added vitamin A and ...
22 Milk, nonfat, fluid, with added nonfat milk so...
23 Milk, nonfat, fluid, protein fortified, with a...
24 Milk, buttermilk, fluid, cultured, lowfat
25 Milk, low sodium, fluid
26 Milk, dry, whole, with added vitamin D
27 Milk, dry, nonfat, regular, without added vita...
28 Milk, dry, nonfat, instant, with added vitamin...
29 Milk, dry, nonfat, calcium reduced
... ...
6606 Beef, tenderloin, steak, separable lean only, ...
6607 Beef, top sirloin, steak, separable lean only,...
6608 Beef, short loin, top loin, steak, separable l...
6609 Beef, chuck, arm pot roast, separable lean onl...
6610 Beef, brisket, flat half, separable lean only,...
6611 Beef, chuck, arm pot roast, separable lean onl...
6612 Beef, brisket, flat half, separable lean only,...
6613 Beef, round, eye of round, roast, separable le...
6614 Beef, round, top round, steak, separable lean ...
6615 Beef, round, bottom round, roast, separable le...
6616 Beef, rib, small end (ribs 10-12), separable l...
6617 CAMPBELL Soup Company, CAMPBELL'S Red and Whit...
6618 CAMPBELL Soup Company, CAMPBELL's Red and Whit...
6619 CAMPBELL Soup Company, CAMPBELL'S SELECT Soups...
6620 CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...
6621 CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...
6622 CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...
6623 CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...
6624 CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...
6625 CAMPBELL Soup Company, CAMPBELL'S Red and Whit...
6626 CAMPBELL Soup Company, V8 Vegetable Juice, Ess...
6627 CAMPBELL Soup Company, V8 Vegetable Juice, Spi...
6628 CAMPBELL Soup Company, PACE, Jalapenos Nacho S...
6629 CAMPBELL Soup Company, V8 60% Vegetable Juice,...
6630 CAMPBELL Soup Company, V8 Vegetable Juice, Low...
6631 Bologna, beef, low fat
6632 Turkey and pork sausage, fresh, bulk, patty or...
6633 Babyfood, juice, pear
6634 Babyfood, dessert, banana yogurt, strained
6635 Babyfood, banana no tapioca, strained fgroup id manufacturer
0 Dairy and Egg Products 1008
1 Dairy and Egg Products 1009
2 Dairy and Egg Products 1018
3 Dairy and Egg Products 1019
4 Dairy and Egg Products 1028
5 Dairy and Egg Products 1029
6 Dairy and Egg Products 1038
7 Dairy and Egg Products 1039
8 Dairy and Egg Products 1048
9 Dairy and Egg Products 1049
10 Dairy and Egg Products 1058
11 Dairy and Egg Products 1059
12 Dairy and Egg Products 1068
13 Dairy and Egg Products 1069
14 Dairy and Egg Products 1078
15 Dairy and Egg Products 1079 None
16 Dairy and Egg Products 1080
17 Dairy and Egg Products 1081
18 Dairy and Egg Products 1082
19 Dairy and Egg Products 1083
20 Dairy and Egg Products 1084
21 Dairy and Egg Products 1085
22 Dairy and Egg Products 1086
23 Dairy and Egg Products 1087
24 Dairy and Egg Products 1088
25 Dairy and Egg Products 1089
26 Dairy and Egg Products 1090
27 Dairy and Egg Products 1091
28 Dairy and Egg Products 1092
29 Dairy and Egg Products 1093
... ... ... ...
6606 Beef Products 23628
6607 Beef Products 23629
6608 Beef Products 23630
6609 Beef Products 23631
6610 Beef Products 23632
6611 Beef Products 23633
6612 Beef Products 23634
6613 Beef Products 23635
6614 Beef Products 23636
6615 Beef Products 23637
6616 Beef Products 23638
6617 Soups, Sauces, and Gravies 27015 Campbell Soup Co.
6618 Soups, Sauces, and Gravies 27016 Campbell Soup Co.
6619 Soups, Sauces, and Gravies 27021 Campbell Soup Co.
6620 Soups, Sauces, and Gravies 27022 Campbell Soup Co.
6621 Soups, Sauces, and Gravies 27023 Campbell Soup Co.
6622 Soups, Sauces, and Gravies 27024 Campbell Soup Co.
6623 Soups, Sauces, and Gravies 27025 Campbell Soup Co.
6624 Soups, Sauces, and Gravies 27026 Campbell Soup Co.
6625 Soups, Sauces, and Gravies 27032 Campbell Soup Co.
6626 Vegetables and Vegetable Products 31010 Campbell Soup Co.
6627 Vegetables and Vegetable Products 31013 Campbell Soup Co.
6628 Vegetables and Vegetable Products 31014 Campbell Soup Co.
6629 Vegetables and Vegetable Products 31016 Campbell Soup Co.
6630 Vegetables and Vegetable Products 31017 Campbell Soup Co.
6631 Sausages and Luncheon Meats 42161
6632 Sausages and Luncheon Meats 42173
6633 Baby Foods 43408 None
6634 Baby Foods 43539 None
6635 Baby Foods 43546 None [6636 rows x 4 columns]
col_mapping = {'description': 'nutrient','group': 'nutgroup'}
nutrients = nutrients.rename(columns=col_mapping, copy=False)
print(nutrients)
nutrient nutgroup units value \
0 Protein Composition g 25.180
1 Total lipid (fat) Composition g 29.200
2 Carbohydrate, by difference Composition g 3.060
3 Ash Other g 3.280
4 Energy Energy kcal 376.000
5 Water Composition g 39.280
6 Energy Energy kJ 1573.000
7 Fiber, total dietary Composition g 0.000
8 Calcium, Ca Elements mg 673.000
9 Iron, Fe Elements mg 0.640
10 Magnesium, Mg Elements mg 22.000
11 Phosphorus, P Elements mg 490.000
12 Potassium, K Elements mg 93.000
13 Sodium, Na Elements mg 690.000
14 Zinc, Zn Elements mg 2.940
15 Copper, Cu Elements mg 0.024
16 Manganese, Mn Elements mg 0.021
17 Selenium, Se Elements mcg 14.500
18 Vitamin A, IU Vitamins IU 1054.000
19 Retinol Vitamins mcg 262.000
20 Vitamin A, RAE Vitamins mcg_RAE 271.000
21 Vitamin C, total ascorbic acid Vitamins mg 0.000
22 Thiamin Vitamins mg 0.031
23 Riboflavin Vitamins mg 0.450
24 Niacin Vitamins mg 0.180
25 Pantothenic acid Vitamins mg 0.190
26 Vitamin B-6 Vitamins mg 0.074
27 Folate, total Vitamins mcg 18.000
28 Vitamin B-12 Vitamins mcg 0.270
29 Folic acid Vitamins mcg 0.000
... ... ... ... ...
389325 Selenium, Se Elements mcg 1.100
389326 Vitamin A, IU Vitamins IU 5.000
389327 Retinol Vitamins mcg 0.000
389328 Vitamin A, RAE Vitamins mcg_RAE 0.000
389329 Carotene, beta Vitamins mcg 2.000
389330 Carotene, alpha Vitamins mcg 2.000
389331 Vitamin E (alpha-tocopherol) Vitamins mg 0.250
389332 Vitamin D Vitamins IU 0.000
389333 Vitamin D (D2 + D3) Vitamins mcg 0.000
389334 Cryptoxanthin, beta Vitamins mcg 0.000
389335 Lycopene Vitamins mcg 0.000
389336 Lutein + zeaxanthin Vitamins mcg 20.000
389337 Vitamin C, total ascorbic acid Vitamins mg 21.900
389338 Thiamin Vitamins mg 0.020
389339 Riboflavin Vitamins mg 0.060
389340 Niacin Vitamins mg 0.540
389341 Vitamin B-6 Vitamins mg 0.260
389342 Folate, total Vitamins mcg 17.000
389343 Vitamin B-12 Vitamins mcg 0.000
389344 Choline, total Vitamins mg 4.100
389345 Vitamin K (phylloquinone) Vitamins mcg 0.500
389346 Folic acid Vitamins mcg 0.000
389347 Folate, food Vitamins mcg 17.000
389348 Folate, DFE Vitamins mcg_DFE 17.000
389349 Vitamin E, added Vitamins mg 0.000
389350 Vitamin B-12, added Vitamins mcg 0.000
389351 Cholesterol Other mg 0.000
389352 Fatty acids, total saturated Other g 0.072
389353 Fatty acids, total monounsaturated Other g 0.028
389354 Fatty acids, total polyunsaturated Other g 0.041 id
0 1008
1 1008
2 1008
3 1008
4 1008
5 1008
6 1008
7 1008
8 1008
9 1008
10 1008
11 1008
12 1008
13 1008
14 1008
15 1008
16 1008
17 1008
18 1008
19 1008
20 1008
21 1008
22 1008
23 1008
24 1008
25 1008
26 1008
27 1008
28 1008
29 1008
... ...
389325 43546
389326 43546
389327 43546
389328 43546
389329 43546
389330 43546
389331 43546
389332 43546
389333 43546
389334 43546
389335 43546
389336 43546
389337 43546
389338 43546
389339 43546
389340 43546
389341 43546
389342 43546
389343 43546
389344 43546
389345 43546
389346 43546
389347 43546
389348 43546
389349 43546
389350 43546
389351 43546
389352 43546
389353 43546
389354 43546 [375176 rows x 5 columns]
ndata = pd.merge(nutrients, info, on='id', how='outer')
print(ndata)
nutrient nutgroup units value \
0 Protein Composition g 25.180
1 Total lipid (fat) Composition g 29.200
2 Carbohydrate, by difference Composition g 3.060
3 Ash Other g 3.280
4 Energy Energy kcal 376.000
5 Water Composition g 39.280
6 Energy Energy kJ 1573.000
7 Fiber, total dietary Composition g 0.000
8 Calcium, Ca Elements mg 673.000
9 Iron, Fe Elements mg 0.640
10 Magnesium, Mg Elements mg 22.000
11 Phosphorus, P Elements mg 490.000
12 Potassium, K Elements mg 93.000
13 Sodium, Na Elements mg 690.000
14 Zinc, Zn Elements mg 2.940
15 Copper, Cu Elements mg 0.024
16 Manganese, Mn Elements mg 0.021
17 Selenium, Se Elements mcg 14.500
18 Vitamin A, IU Vitamins IU 1054.000
19 Retinol Vitamins mcg 262.000
20 Vitamin A, RAE Vitamins mcg_RAE 271.000
21 Vitamin C, total ascorbic acid Vitamins mg 0.000
22 Thiamin Vitamins mg 0.031
23 Riboflavin Vitamins mg 0.450
24 Niacin Vitamins mg 0.180
25 Pantothenic acid Vitamins mg 0.190
26 Vitamin B-6 Vitamins mg 0.074
27 Folate, total Vitamins mcg 18.000
28 Vitamin B-12 Vitamins mcg 0.270
29 Folic acid Vitamins mcg 0.000
... ... ... ... ...
375146 Selenium, Se Elements mcg 1.100
375147 Vitamin A, IU Vitamins IU 5.000
375148 Retinol Vitamins mcg 0.000
375149 Vitamin A, RAE Vitamins mcg_RAE 0.000
375150 Carotene, beta Vitamins mcg 2.000
375151 Carotene, alpha Vitamins mcg 2.000
375152 Vitamin E (alpha-tocopherol) Vitamins mg 0.250
375153 Vitamin D Vitamins IU 0.000
375154 Vitamin D (D2 + D3) Vitamins mcg 0.000
375155 Cryptoxanthin, beta Vitamins mcg 0.000
375156 Lycopene Vitamins mcg 0.000
375157 Lutein + zeaxanthin Vitamins mcg 20.000
375158 Vitamin C, total ascorbic acid Vitamins mg 21.900
375159 Thiamin Vitamins mg 0.020
375160 Riboflavin Vitamins mg 0.060
375161 Niacin Vitamins mg 0.540
375162 Vitamin B-6 Vitamins mg 0.260
375163 Folate, total Vitamins mcg 17.000
375164 Vitamin B-12 Vitamins mcg 0.000
375165 Choline, total Vitamins mg 4.100
375166 Vitamin K (phylloquinone) Vitamins mcg 0.500
375167 Folic acid Vitamins mcg 0.000
375168 Folate, food Vitamins mcg 17.000
375169 Folate, DFE Vitamins mcg_DFE 17.000
375170 Vitamin E, added Vitamins mg 0.000
375171 Vitamin B-12, added Vitamins mcg 0.000
375172 Cholesterol Other mg 0.000
375173 Fatty acids, total saturated Other g 0.072
375174 Fatty acids, total monounsaturated Other g 0.028
375175 Fatty acids, total polyunsaturated Other g 0.041 id food fgroup \
0 1008 Cheese, caraway Dairy and Egg Products
1 1008 Cheese, caraway Dairy and Egg Products
2 1008 Cheese, caraway Dairy and Egg Products
3 1008 Cheese, caraway Dairy and Egg Products
4 1008 Cheese, caraway Dairy and Egg Products
5 1008 Cheese, caraway Dairy and Egg Products
6 1008 Cheese, caraway Dairy and Egg Products
7 1008 Cheese, caraway Dairy and Egg Products
8 1008 Cheese, caraway Dairy and Egg Products
9 1008 Cheese, caraway Dairy and Egg Products
10 1008 Cheese, caraway Dairy and Egg Products
11 1008 Cheese, caraway Dairy and Egg Products
12 1008 Cheese, caraway Dairy and Egg Products
13 1008 Cheese, caraway Dairy and Egg Products
14 1008 Cheese, caraway Dairy and Egg Products
15 1008 Cheese, caraway Dairy and Egg Products
16 1008 Cheese, caraway Dairy and Egg Products
17 1008 Cheese, caraway Dairy and Egg Products
18 1008 Cheese, caraway Dairy and Egg Products
19 1008 Cheese, caraway Dairy and Egg Products
20 1008 Cheese, caraway Dairy and Egg Products
21 1008 Cheese, caraway Dairy and Egg Products
22 1008 Cheese, caraway Dairy and Egg Products
23 1008 Cheese, caraway Dairy and Egg Products
24 1008 Cheese, caraway Dairy and Egg Products
25 1008 Cheese, caraway Dairy and Egg Products
26 1008 Cheese, caraway Dairy and Egg Products
27 1008 Cheese, caraway Dairy and Egg Products
28 1008 Cheese, caraway Dairy and Egg Products
29 1008 Cheese, caraway Dairy and Egg Products
... ... ... ...
375146 43546 Babyfood, banana no tapioca, strained Baby Foods
375147 43546 Babyfood, banana no tapioca, strained Baby Foods
375148 43546 Babyfood, banana no tapioca, strained Baby Foods
375149 43546 Babyfood, banana no tapioca, strained Baby Foods
375150 43546 Babyfood, banana no tapioca, strained Baby Foods
375151 43546 Babyfood, banana no tapioca, strained Baby Foods
375152 43546 Babyfood, banana no tapioca, strained Baby Foods
375153 43546 Babyfood, banana no tapioca, strained Baby Foods
375154 43546 Babyfood, banana no tapioca, strained Baby Foods
375155 43546 Babyfood, banana no tapioca, strained Baby Foods
375156 43546 Babyfood, banana no tapioca, strained Baby Foods
375157 43546 Babyfood, banana no tapioca, strained Baby Foods
375158 43546 Babyfood, banana no tapioca, strained Baby Foods
375159 43546 Babyfood, banana no tapioca, strained Baby Foods
375160 43546 Babyfood, banana no tapioca, strained Baby Foods
375161 43546 Babyfood, banana no tapioca, strained Baby Foods
375162 43546 Babyfood, banana no tapioca, strained Baby Foods
375163 43546 Babyfood, banana no tapioca, strained Baby Foods
375164 43546 Babyfood, banana no tapioca, strained Baby Foods
375165 43546 Babyfood, banana no tapioca, strained Baby Foods
375166 43546 Babyfood, banana no tapioca, strained Baby Foods
375167 43546 Babyfood, banana no tapioca, strained Baby Foods
375168 43546 Babyfood, banana no tapioca, strained Baby Foods
375169 43546 Babyfood, banana no tapioca, strained Baby Foods
375170 43546 Babyfood, banana no tapioca, strained Baby Foods
375171 43546 Babyfood, banana no tapioca, strained Baby Foods
375172 43546 Babyfood, banana no tapioca, strained Baby Foods
375173 43546 Babyfood, banana no tapioca, strained Baby Foods
375174 43546 Babyfood, banana no tapioca, strained Baby Foods
375175 43546 Babyfood, banana no tapioca, strained Baby Foods manufacturer
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
... ...
375146 None
375147 None
375148 None
375149 None
375150 None
375151 None
375152 None
375153 None
375154 None
375155 None
375156 None
375157 None
375158 None
375159 None
375160 None
375161 None
375162 None
375163 None
375164 None
375165 None
375166 None
375167 None
375168 None
375169 None
375170 None
375171 None
375172 None
375173 None
375174 None
375175 None [375176 rows x 8 columns]
ndata.loc[30000]
nutrient Glycine
nutgroup Amino Acids
units g
value 0.04
id 6158
food Soup, tomato bisque, canned, condensed
fgroup Soups, Sauces, and Gravies
manufacturer
Name: 30000, dtype: object
result = ndata.groupby(['nutrient', 'fgroup'])['value'].quantile(0.5)
print(result)
nutrient fgroup
Adjusted Protein Sweets 12.9000Vegetables and Vegetable Products 2.1800
Alanine Baby Foods 0.0850Baked Products 0.2480Beef Products 1.5500Beverages 0.0030Breakfast Cereals 0.3110Cereal Grains and Pasta 0.3730Dairy and Egg Products 0.2710Ethnic Foods 1.2900Fast Foods 0.5140Fats and Oils 0.0000Finfish and Shellfish Products 1.2180Fruits and Fruit Juices 0.0270Lamb, Veal, and Game Products 1.4080Legumes and Legume Products 0.4100Meals, Entrees, and Sidedishes 0.3270Nut and Seed Products 0.7345Pork Products 1.3070Poultry Products 1.3940Restaurant Foods 0.4650Sausages and Luncheon Meats 0.9420Snacks 0.4335Soups, Sauces, and Gravies 0.0650Spices and Herbs 0.5550Sweets 0.1020Vegetables and Vegetable Products 0.0840
Alcohol, ethyl Baby Foods 0.0000Baked Products 0.0000Beef Products 0.0000...
Water Snacks 3.5200Soups, Sauces, and Gravies 85.9000Spices and Herbs 43.6700Sweets 9.0500Vegetables and Vegetable Products 89.1950
Zinc, Zn Baby Foods 0.5900Baked Products 0.6600Beef Products 5.3900Beverages 0.0400Breakfast Cereals 2.8850Cereal Grains and Pasta 1.0900Dairy and Egg Products 1.3900Ethnic Foods 1.0450Fast Foods 1.2500Fats and Oils 0.0200Finfish and Shellfish Products 0.6700Fruits and Fruit Juices 0.1000Lamb, Veal, and Game Products 3.9400Legumes and Legume Products 1.1400Meals, Entrees, and Sidedishes 0.6300Nut and Seed Products 3.2900Pork Products 2.3200Poultry Products 2.5000Restaurant Foods 0.8000Sausages and Luncheon Meats 2.1300Snacks 1.4700Soups, Sauces, and Gravies 0.2000Spices and Herbs 2.7500Sweets 0.3600Vegetables and Vegetable Products 0.3300
Name: value, Length: 2246, dtype: float64
# result['Zinc, Zn'].order().plot(kind='barh') #AttributeError: 'Series' object has no attribute 'order'
result['Zinc, Zn'].sort_values().plot(kind='barh')
<matplotlib.axes._subplots.AxesSubplot at 0x2156ce3c748>
by_nutrient = ndata.groupby(['nutgroup', 'nutrient'])get_maximum = lambda x: x.xs(x.value.idxmax())
get_minimum = lambda x: x.xs(x.value.idxmin())max_foods = by_nutrient.apply(get_maximum)[['value', 'food']]
print(max_foods)
value \
nutgroup nutrient
Amino Acids Alanine 8.009 Arginine 7.436 Aspartic acid 10.203 Cystine 1.307 Glutamic acid 17.452 Glycine 19.049 Histidine 2.999 Hydroxyproline 0.803 Isoleucine 4.300 Leucine 7.200 Lysine 6.690 Methionine 1.859 Phenylalanine 4.600 Proline 12.295 Serine 4.600 Threonine 3.300 Tryptophan 1.600 Tyrosine 3.300 Valine 4.500
Composition Adjusted Protein 12.900 Carbohydrate, by difference 100.000 Fiber, total dietary 79.000 Protein 88.320 Sugars, total 99.800 Total lipid (fat) 100.000 Water 100.000
Elements Calcium, Ca 7364.000 Copper, Cu 15.050 Fluoride, F 584.000 Iron, Fe 87.470
... ...
Vitamins Cryptoxanthin, beta 6186.000 Dihydrophylloquinone 103.800 Folate, DFE 2630.000 Folate, food 2340.000 Folate, total 2340.000 Folic acid 1538.000 Lutein + zeaxanthin 39550.000 Lycopene 46260.000 Menaquinone-4 33.200 Niacin 97.000 Pantothenic acid 35.000 Retinol 30000.000 Riboflavin 14.300 Thiamin 20.000 Tocopherol, beta 6.490 Tocopherol, delta 30.880 Tocopherol, gamma 100.880 Vitamin A, IU 100000.000 Vitamin A, RAE 30000.000 Vitamin B-12 98.890 Vitamin B-12, added 24.000 Vitamin B-6 12.000 Vitamin C, total ascorbic acid 2400.000 Vitamin D 10000.000 Vitamin D (D2 + D3) 250.000 Vitamin D2 (ergocalciferol) 28.100 Vitamin D3 (cholecalciferol) 27.400 Vitamin E (alpha-tocopherol) 149.400 Vitamin E, added 46.550 Vitamin K (phylloquinone) 1714.500 food
nutgroup nutrient
Amino Acids Alanine Gelatins, dry powder, unsweetened Arginine Seeds, sesame flour, low-fat Aspartic acid Soy protein isolate Cystine Seeds, cottonseed flour, low fat (glandless) Glutamic acid Soy protein isolate Glycine Gelatins, dry powder, unsweetened Histidine Whale, beluga, meat, dried (Alaska Native) Hydroxyproline KENTUCKY FRIED CHICKEN, Fried Chicken, ORIGINA... Isoleucine Soy protein isolate, PROTEIN TECHNOLOGIES INTE... Leucine Soy protein isolate, PROTEIN TECHNOLOGIES INTE... Lysine Seal, bearded (Oogruk), meat, dried (Alaska Na... Methionine Fish, cod, Atlantic, dried and salted Phenylalanine Soy protein isolate, PROTEIN TECHNOLOGIES INTE... Proline Gelatins, dry powder, unsweetened Serine Soy protein isolate, PROTEIN TECHNOLOGIES INTE... Threonine Soy protein isolate, PROTEIN TECHNOLOGIES INTE... Tryptophan Sea lion, Steller, meat with fat (Alaska Native) Tyrosine Soy protein isolate, PROTEIN TECHNOLOGIES INTE... Valine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Composition Adjusted Protein Baking chocolate, unsweetened, squares Carbohydrate, by difference Sweeteners, tabletop, fructose, dry, powder Fiber, total dietary Corn bran, crude Protein Soy protein isolate, potassium type, crude pro... Sugars, total Sugars, granulated Total lipid (fat) Oil, wheat germ Water Water, bottled, POLAND SPRING
Elements Calcium, Ca Leavening agents, baking powder, double-acting... Copper, Cu Veal, variety meats and by-products, liver, co... Fluoride, F Tea, instant, sweetened with sugar, lemon-flav... Iron, Fe Salad dressing, russian dressing, low calorie
... ...
Vitamins Cryptoxanthin, beta Spices, paprika Dihydrophylloquinone Margarine, 80% fat, stick, includes regular an... Folate, DFE Cereals ready-to-eat, QUAKER, CAP'N CRUNCH Folate, food Leavening agents, yeast, baker's, active dry Folate, total Leavening agents, yeast, baker's, active dry Folic acid Cereals ready-to-eat, QUAKER, CAP'N CRUNCH Lutein + zeaxanthin Kale, raw Lycopene Tomato powder Menaquinone-4 Chicken, broilers or fryers, drumstick, meat a... Niacin Yeast extract spread Pantothenic acid Cereals ready-to-eat, KELLOGG, KELLOGG'S Compl... Retinol Fish oil, cod liver Riboflavin Yeast extract spread Thiamin MORNINGSTAR FARMS Hot and Spicy Veggie Sausage... Tocopherol, beta Yellow pond lily, Wocas, dried seeds (Pacific ... Tocopherol, delta Oil, cooking and salad, ENOVA, 80% diglycerides Tocopherol, gamma Oil, cooking and salad, ENOVA, 80% diglycerides Vitamin A, IU Fish oil, cod liver Vitamin A, RAE Fish oil, cod liver Vitamin B-12 Mollusks, clam, mixed species, cooked, moist heat Vitamin B-12, added Cereals ready-to-eat, KELLOGG, KELLOGG'S ALL-B... Vitamin B-6 Cereals ready-to-eat, KELLOGG, KELLOGG'S ALL-B... Vitamin C, total ascorbic acid Orange-flavor drink, breakfast type, low calor... Vitamin D Fish oil, cod liver Vitamin D (D2 + D3) Fish oil, cod liver Vitamin D2 (ergocalciferol) Mushrooms, maitake, raw Vitamin D3 (cholecalciferol) Fish, halibut, Greenland, raw Vitamin E (alpha-tocopherol) Oil, wheat germ Vitamin E, added Cereals ready-to-eat, GENERAL MILLS, Multi-Gra... Vitamin K (phylloquinone) Spices, sage, ground [94 rows x 2 columns]
max_foods.loc['Amino Acids']['food']
nutrient
Alanine Gelatins, dry powder, unsweetened
Arginine Seeds, sesame flour, low-fat
Aspartic acid Soy protein isolate
Cystine Seeds, cottonseed flour, low fat (glandless)
Glutamic acid Soy protein isolate
Glycine Gelatins, dry powder, unsweetened
Histidine Whale, beluga, meat, dried (Alaska Native)
Hydroxyproline KENTUCKY FRIED CHICKEN, Fried Chicken, ORIGINA...
Isoleucine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Leucine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Lysine Seal, bearded (Oogruk), meat, dried (Alaska Na...
Methionine Fish, cod, Atlantic, dried and salted
Phenylalanine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Proline Gelatins, dry powder, unsweetened
Serine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Threonine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Tryptophan Sea lion, Steller, meat with fat (Alaska Native)
Tyrosine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Valine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Name: food, dtype: object
参考资料：炼数成金Python数据分析课程
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