文章目录

  • Pandas 学习
    • 核心数据结构
      • DataFrame
        • 属性
      • Panel
      • Series
    • 基本数据操作
    • 运算

Pandas 学习

核心数据结构

DataFrame
import numpy as np
data = np.random.normal(0,1,(10,5))
data

array([[ 1.21218494, -0.30678674, -0.66781485,  0.13414137,  0.79799153],[-1.35105539,  1.26378017,  0.04932303, -0.93626636, -0.1161889 ],[-0.01031587, -0.5311115 ,  0.17573352,  1.44961339,  0.9510395 ],[-0.38485007, -0.11271772,  0.86289105, -0.7046349 , -0.67357859],[-0.23342328, -1.15661425,  1.729237  , -1.1405832 , -0.06817234],[-1.12947171, -0.28125216, -0.71016417, -0.22660793, -1.88541989],[-0.02421679,  0.45015635, -0.57010218,  1.00306539,  0.49455672],[ 1.12410909, -0.59830918, -0.96323314,  1.2907916 ,  1.0353455 ],[-1.5828247 , -0.60952617,  1.10117806,  1.57818777,  1.69498839],[-0.02737215,  0.51650423, -0.12322063,  1.84409656, -1.20511615]])

import pandas as pd
pd.DataFrame(data)
0 1 2 3 4
0 1.212185 -0.306787 -0.667815 0.134141 0.797992
1 -1.351055 1.263780 0.049323 -0.936266 -0.116189
2 -0.010316 -0.531111 0.175734 1.449613 0.951039
3 -0.384850 -0.112718 0.862891 -0.704635 -0.673579
4 -0.233423 -1.156614 1.729237 -1.140583 -0.068172
5 -1.129472 -0.281252 -0.710164 -0.226608 -1.885420
6 -0.024217 0.450156 -0.570102 1.003065 0.494557
7 1.124109 -0.598309 -0.963233 1.290792 1.035346
8 -1.582825 -0.609526 1.101178 1.578188 1.694988
9 -0.027372 0.516504 -0.123221 1.844097 -1.205116 
# 添加行索引
num = ["num{}".format(i) for i in range(10)]
pd.DataFrame(data,index=num)
0 1 2 3 4
num0 1.212185 -0.306787 -0.667815 0.134141 0.797992
num1 -1.351055 1.263780 0.049323 -0.936266 -0.116189
num2 -0.010316 -0.531111 0.175734 1.449613 0.951039
num3 -0.384850 -0.112718 0.862891 -0.704635 -0.673579
num4 -0.233423 -1.156614 1.729237 -1.140583 -0.068172
num5 -1.129472 -0.281252 -0.710164 -0.226608 -1.885420
num6 -0.024217 0.450156 -0.570102 1.003065 0.494557
num7 1.124109 -0.598309 -0.963233 1.290792 1.035346
num8 -1.582825 -0.609526 1.101178 1.578188 1.694988
num9 -0.027372 0.516504 -0.123221 1.844097 -1.205116 
# 添加列索引
num2 = ["index{}".format(i) for i in range(5)]
data = pd.DataFrame(data,index=num,columns=num2)
data
index0 index1 index2 index3 index4
num0 1.212185 -0.306787 -0.667815 0.134141 0.797992
num1 -1.351055 1.263780 0.049323 -0.936266 -0.116189
num2 -0.010316 -0.531111 0.175734 1.449613 0.951039
num3 -0.384850 -0.112718 0.862891 -0.704635 -0.673579
num4 -0.233423 -1.156614 1.729237 -1.140583 -0.068172
num5 -1.129472 -0.281252 -0.710164 -0.226608 -1.885420
num6 -0.024217 0.450156 -0.570102 1.003065 0.494557
num7 1.124109 -0.598309 -0.963233 1.290792 1.035346
num8 -1.582825 -0.609526 1.101178 1.578188 1.694988
num9 -0.027372 0.516504 -0.123221 1.844097 -1.205116
属性
data.shape

(10, 5)

data.index

Index(['num0', 'num1', 'num2', 'num3', 'num4', 'num5', 'num6', 'num7', 'num8','num9'],dtype='object')

data.columns

Index(['index0', 'index1', 'index2', 'index3', 'index4'], dtype='object')

data.values

array([[ 1.21218494, -0.30678674, -0.66781485,  0.13414137,  0.79799153],[-1.35105539,  1.26378017,  0.04932303, -0.93626636, -0.1161889 ],[-0.01031587, -0.5311115 ,  0.17573352,  1.44961339,  0.9510395 ],[-0.38485007, -0.11271772,  0.86289105, -0.7046349 , -0.67357859],[-0.23342328, -1.15661425,  1.729237  , -1.1405832 , -0.06817234],[-1.12947171, -0.28125216, -0.71016417, -0.22660793, -1.88541989],[-0.02421679,  0.45015635, -0.57010218,  1.00306539,  0.49455672],[ 1.12410909, -0.59830918, -0.96323314,  1.2907916 ,  1.0353455 ],[-1.5828247 , -0.60952617,  1.10117806,  1.57818777,  1.69498839],[-0.02737215,  0.51650423, -0.12322063,  1.84409656, -1.20511615]])

data.T
num0 num1 num2 num3 num4 num5 num6 num7 num8 num9
index0 1.212185 -1.351055 -0.010316 -0.384850 -0.233423 -1.129472 -0.024217 1.124109 -1.582825 -0.027372
index1 -0.306787 1.263780 -0.531111 -0.112718 -1.156614 -0.281252 0.450156 -0.598309 -0.609526 0.516504
index2 -0.667815 0.049323 0.175734 0.862891 1.729237 -0.710164 -0.570102 -0.963233 1.101178 -0.123221
index3 0.134141 -0.936266 1.449613 -0.704635 -1.140583 -0.226608 1.003065 1.290792 1.578188 1.844097
index4 0.797992 -0.116189 0.951039 -0.673579 -0.068172 -1.885420 0.494557 1.035346 1.694988 -1.205116 
data.head()
index0 index1 index2 index3 index4
num0 1.212185 -0.306787 -0.667815 0.134141 0.797992
num1 -1.351055 1.263780 0.049323 -0.936266 -0.116189
num2 -0.010316 -0.531111 0.175734 1.449613 0.951039
num3 -0.384850 -0.112718 0.862891 -0.704635 -0.673579
num4 -0.233423 -1.156614 1.729237 -1.140583 -0.068172 
data.tail()
index0 index1 index2 index3 index4
num5 -1.129472 -0.281252 -0.710164 -0.226608 -1.885420
num6 -0.024217 0.450156 -0.570102 1.003065 0.494557
num7 1.124109 -0.598309 -0.963233 1.290792 1.035346
num8 -1.582825 -0.609526 1.101178 1.578188 1.694988
num9 -0.027372 0.516504 -0.123221 1.844097 -1.205116 
# 重设索引
data.reset_index()
index index0 index1 index2 index3 index4
0 num0 1.212185 -0.306787 -0.667815 0.134141 0.797992
1 num1 -1.351055 1.263780 0.049323 -0.936266 -0.116189
2 num2 -0.010316 -0.531111 0.175734 1.449613 0.951039
3 num3 -0.384850 -0.112718 0.862891 -0.704635 -0.673579
4 num4 -0.233423 -1.156614 1.729237 -1.140583 -0.068172
5 num5 -1.129472 -0.281252 -0.710164 -0.226608 -1.885420
6 num6 -0.024217 0.450156 -0.570102 1.003065 0.494557
7 num7 1.124109 -0.598309 -0.963233 1.290792 1.035346
8 num8 -1.582825 -0.609526 1.101178 1.578188 1.694988
9 num9 -0.027372 0.516504 -0.123221 1.844097 -1.205116 
# 设置新索引
df = pd.DataFrame({'month': [1, 4, 7, 10],'year': [2012, 2014, 2013, 2014],'sale':[55, 40, 84, 31]})
df
month year sale
0 1 2012 55
1 4 2014 40
2 7 2013 84
3 10 2014 31 
# 以月份设置新的索引
df.set_index("month", drop=True)
year sale
month
1 2012 55
4 2014 40
7 2013 84
10 2014 31 
# 设置多个索引，以年和月份
new_df = df.set_index(["year", "month"])
new_df.index

MultiIndex(levels=[[2012, 2013, 2014], [1, 4, 7, 10]],labels=[[0, 2, 1, 2], [0, 1, 2, 3]],names=['year', 'month'])

new_df.index.names # levels的名称

FrozenList(['year', 'month'])

new_df.index.levels # 每个level的元组值

FrozenList([[2012, 2013, 2014], [1, 4, 7, 10]])
Panel
# panel 三维数据，需要从不同维度访问
p = pd.Panel(np.arange(24).reshape(4,3,2),items=list('ABCD'),major_axis=pd.date_range('20130101', periods=3),minor_axis=['first', 'second'])
p

C:\Users\28599\AppData\Roaming\Python\Python37\site-packages\IPython\core\interactiveshell.py:3418: FutureWarning:
Panel is deprecated and will be removed in a future version.
The recommended way to represent these types of 3-dimensional data are with a MultiIndex on a DataFrame, via the Panel.to_frame() method
Alternatively, you can use the xarray package http://xarray.pydata.org/en/stable/.
Pandas provides a `.to_xarray()` method to help automate this conversion.exec(code_obj, self.user_global_ns, self.user_ns)<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 3 (major_axis) x 2 (minor_axis)
Items axis: A to D
Major_axis axis: 2013-01-01 00:00:00 to 2013-01-03 00:00:00
Minor_axis axis: first to second

p["A"]
first second
2013-01-01 0 1
2013-01-02 2 3
2013-01-03 4 5 
p.major_xs("2013-01-03")
A B C D
first 4 10 16 22
second 5 11 17 23 
p.minor_xs("first")
A B C D
2013-01-01 0 6 12 18
2013-01-02 2 8 14 20
2013-01-03 4 10 16 22
Series
# Series 带索引的一维数组
sr = data.iloc[1,:]
sr

index0   -1.351055
index1    1.263780
index2    0.049323
index3   -0.936266
index4   -0.116189
Name: num1, dtype: float64

sr.index

Index(['index0', 'index1', 'index2', 'index3', 'index4'], dtype='object')

sr.values

array([-1.35105539,  1.26378017,  0.04932303, -0.93626636, -0.1161889 ])

pd.Series(np.arange(10))

0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
dtype: int32

pd.Series(np.arange(3, 9, 2), index=["a", "b", "c"])

a    3
b    5
c    7
dtype: int32

基本数据操作

data = pd.read_csv("./stock_day.csv")
data.head()
open high close low volume price_change p_change ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 turnover
2018-02-27 23.53 25.88 24.16 23.53 95578.03 0.63 2.68 22.942 22.142 22.875 53782.64 46738.65 55576.11 2.39
2018-02-26 22.80 23.78 23.53 22.80 60985.11 0.69 3.02 22.406 21.955 22.942 40827.52 42736.34 56007.50 1.53
2018-02-23 22.88 23.37 22.82 22.71 52914.01 0.54 2.42 21.938 21.929 23.022 35119.58 41871.97 56372.85 1.32
2018-02-22 22.25 22.76 22.28 22.02 36105.01 0.36 1.64 21.446 21.909 23.137 35397.58 39904.78 60149.60 0.90
2018-02-14 21.49 21.99 21.92 21.48 23331.04 0.44 2.05 21.366 21.923 23.253 33590.21 42935.74 61716.11 0.58 
data = data.drop(["ma5","ma10","ma20","v_ma5","v_ma10","v_ma20"], axis=1)
data.head()
open high close low volume price_change p_change turnover
2018-02-27 23.53 25.88 24.16 23.53 95578.03 0.63 2.68 2.39
2018-02-26 22.80 23.78 23.53 22.80 60985.11 0.69 3.02 1.53
2018-02-23 22.88 23.37 22.82 22.71 52914.01 0.54 2.42 1.32
2018-02-22 22.25 22.76 22.28 22.02 36105.01 0.36 1.64 0.90
2018-02-14 21.49 21.99 21.92 21.48 23331.04 0.44 2.05 0.58 
# 先列后行
data["open"]["2018-02-26"]

22.8

# 按名字索引
data.loc["2018-02-26"]["open"]
# 同 data.loc["2018-02-26","open"]

22.8

# 按数字索引
data.iloc[1,0]

22.8

# 组合索引
# 获取行第1天到第4天，['open', 'close', 'high', 'low']这个四个指标的结果
# data.ix[:4, ['open', 'close', 'high', 'low']] # 将要废弃，不推荐
data.loc[data.index[0:4], ['open', 'close', 'high', 'low']]
# 同 data.iloc[0:4,data.columns.get_indexer(['open', 'close', 'high', 'low'])]
open close high low
2018-02-27 23.53 24.16 25.88 23.53
2018-02-26 22.80 23.53 23.78 22.80
2018-02-23 22.88 22.82 23.37 22.71
2018-02-22 22.25 22.28 22.76 22.02 
# 赋值操作 (先索引再赋值即可)
data.open=100
data.head()
open high close low volume price_change p_change turnover
2018-02-27 100 25.88 24.16 23.53 95578.03 0.63 2.68 2.39
2018-02-26 100 23.78 23.53 22.80 60985.11 0.69 3.02 1.53
2018-02-23 100 23.37 22.82 22.71 52914.01 0.54 2.42 1.32
2018-02-22 100 22.76 22.28 22.02 36105.01 0.36 1.64 0.90
2018-02-14 100 21.99 21.92 21.48 23331.04 0.44 2.05 0.58 
# 对内容排序
# ascending=True表示升序 high相同就看p_change
data.sort_values(by=['high','p_change'],ascending=True).head()
open high close low volume price_change p_change turnover
2015-03-02 100 12.67 12.52 12.20 96291.73 0.32 2.62 3.30
2015-03-04 100 12.92 12.90 12.61 67075.44 0.20 1.57 2.30
2015-03-03 100 13.06 12.70 12.52 139071.61 0.18 1.44 4.76
2015-09-07 100 13.38 12.77 12.63 52490.04 0.37 2.98 1.80
2015-03-05 100 13.45 13.16 12.87 93180.39 0.26 2.02 3.19 
data.sort_index().head()
open high close low volume price_change p_change turnover
2015-03-02 100 12.67 12.52 12.20 96291.73 0.32 2.62 3.30
2015-03-03 100 13.06 12.70 12.52 139071.61 0.18 1.44 4.76
2015-03-04 100 12.92 12.90 12.61 67075.44 0.20 1.57 2.30
2015-03-05 100 13.45 13.16 12.87 93180.39 0.26 2.02 3.19
2015-03-06 100 14.48 14.28 13.13 179831.72 1.12 8.51 6.16

运算

# 算术运算
data["open"].sub(3).head()
# data.open + 3

2018-02-27    97
2018-02-26    97
2018-02-23    97
2018-02-22    97
2018-02-14    97
Name: open, dtype: int64

# 逻辑运算
data[data['p_change'] > 2].head()
open high close low volume price_change p_change turnover
2018-02-27 100 25.88 24.16 23.53 95578.03 0.63 2.68 2.39
2018-02-26 100 23.78 23.53 22.80 60985.11 0.69 3.02 1.53
2018-02-23 100 23.37 22.82 22.71 52914.01 0.54 2.42 1.32
2018-02-14 100 21.99 21.92 21.48 23331.04 0.44 2.05 0.58
2018-02-12 100 21.40 21.19 20.63 32445.39 0.82 4.03 0.81 
data[(data["p_change"] > 2) & (data["low"] > 15)].head()
open high close low volume price_change p_change turnover
2018-02-27 100 25.88 24.16 23.53 95578.03 0.63 2.68 2.39
2018-02-26 100 23.78 23.53 22.80 60985.11 0.69 3.02 1.53
2018-02-23 100 23.37 22.82 22.71 52914.01 0.54 2.42 1.32
2018-02-14 100 21.99 21.92 21.48 23331.04 0.44 2.05 0.58
2018-02-12 100 21.40 21.19 20.63 32445.39 0.82 4.03 0.81 
# 逻辑运算函数
data.query("p_change > 2 & low > 15").head()
open high close low volume price_change p_change turnover
2018-02-27 100 25.88 24.16 23.53 95578.03 0.63 2.68 2.39
2018-02-26 100 23.78 23.53 22.80 60985.11 0.69 3.02 1.53
2018-02-23 100 23.37 22.82 22.71 52914.01 0.54 2.42 1.32
2018-02-14 100 21.99 21.92 21.48 23331.04 0.44 2.05 0.58
2018-02-12 100 21.40 21.19 20.63 32445.39 0.82 4.03 0.81 
# 判断'turnover'是否为4.19, 2.39
data[data["turnover"].isin([4.19, 2.39])]
open high close low volume price_change p_change turnover
2018-02-27 100 25.88 24.16 23.53 95578.03 0.63 2.68 2.39
2017-07-25 100 24.20 23.70 22.64 167489.48 0.67 2.91 4.19
2016-09-28 100 20.98 20.86 19.71 95580.75 0.98 4.93 2.39
2015-04-07 100 17.98 17.54 16.50 122471.85 0.88 5.28 4.19 
# 统计运算
data.describe()
open high close low volume price_change p_change turnover
count 643.0 643.000000 643.000000 643.000000 643.000000 643.000000 643.000000 643.000000
mean 100.0 21.900513 21.336267 20.771835 99905.519114 0.018802 0.190280 2.936190
std 0.0 4.077578 3.942806 3.791968 73879.119354 0.898476 4.079698 2.079375
min 100.0 12.670000 12.360000 12.200000 1158.120000 -3.520000 -10.030000 0.040000
25% 100.0 19.500000 19.045000 18.525000 48533.210000 -0.390000 -1.850000 1.360000
50% 100.0 21.970000 21.450000 20.980000 83175.930000 0.050000 0.260000 2.500000
75% 100.0 24.065000 23.415000 22.850000 127580.055000 0.455000 2.305000 3.915000
max 100.0 36.350000 35.210000 34.010000 501915.410000 3.030000 10.030000 12.560000 
# 累计统计函数 (类似前缀和)
data["p_change"].cumsum().head()

2018-02-27     2.68
2018-02-26     5.70
2018-02-23     8.12
2018-02-22     9.76
2018-02-14    11.81
Name: p_change, dtype: float64

data.max(axis=0)

open               100.00
high                36.35
close               35.21
low                 34.01
volume          501915.41
price_change         3.03
p_change            10.03
turnover            12.56
dtype: float64

data.idxmax(axis=0)

open            2018-02-27
high            2015-06-10
close           2015-06-12
low             2015-06-12
volume          2017-10-26
price_change    2015-06-09
p_change        2015-08-28
turnover        2017-10-26
dtype: object

# 累计统计函数
data["p_change"].sort_index().cumsum().plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1959a1c6b38>


# 自定义计算
data.apply(lambda x: x.max() - x.min()) # 每一列最大值减最小值

open                 0.00
high                23.68
close               22.85
low                 21.81
volume          500757.29
price_change         6.55
p_change            20.06
turnover            12.52
dtype: float64

data["volume"].max() - data["volume"].min() # volume列最大值减最小值

500757.29

data.plot(x="volume", y="turnover", kind="scatter")

<matplotlib.axes._subplots.AxesSubplot at 0x1959a228438>


data.plot(x="high", y="low", kind="scatter")

<matplotlib.axes._subplots.AxesSubplot at 0x19597e1e3c8>

​

​

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