Pandas 学习笔记二

数据的读取与存储

csv格式
import pandas as pd
# 读取csv文件
data = pd.read_csv("stock_day.csv",usecols=['open','high','low','close'])
data.head()
open high close low
2018-02-27 23.53 25.88 24.16 23.53
2018-02-26 22.80 23.78 23.53 22.80
2018-02-23 22.88 23.37 22.82 22.71
2018-02-22 22.25 22.76 22.28 22.02
2018-02-14 21.49 21.99 21.92 21.48 
data = pd.read_csv("stock_day2.csv", names=["open", "high", "close", "low", "volume", "price_change", "p_change", "ma5", "ma10", "ma20", "v_ma5", "v_ma10", "v_ma20", "turnover"])
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 
# 写入csv文件
# data.to_csv("1.csv")
# 保存'open'列的数据
data[:10].to_csv("test.csv", columns=["open"])
pd.read_csv("test.csv")
Unnamed: 0 open
0 2018-02-27 23.53
1 2018-02-26 22.80
2 2018-02-23 22.88
3 2018-02-22 22.25
4 2018-02-14 21.49
5 2018-02-13 21.40
6 2018-02-12 20.70
7 2018-02-09 21.20
8 2018-02-08 21.79
9 2018-02-07 22.69 
data[:10].to_csv("test.csv", columns=["open"], index=False, mode="a", header=False)
pd.read_csv("test.csv")
Unnamed: 0 open
0 2018-02-27 23.53
1 2018-02-26 22.80
2 2018-02-23 22.88
3 2018-02-22 22.25
4 2018-02-14 21.49
5 2018-02-13 21.40
6 2018-02-12 20.70
7 2018-02-09 21.20
8 2018-02-08 21.79
9 2018-02-07 22.69
10 23.53 NaN
11 22.8 NaN
12 22.88 NaN
13 22.25 NaN
14 21.49 NaN
15 21.4 NaN
16 20.7 NaN
17 21.2 NaN
18 21.79 NaN
19 22.69 NaN
hdf5格式
# 读取hdf5文件
dayClose = pd.read_hdf("day_close.h5")
dayClose.head()
000001.SZ 000002.SZ 000004.SZ 000005.SZ 000006.SZ 000007.SZ 000008.SZ 000009.SZ 000010.SZ 000011.SZ ... 001965.SZ 603283.SH 002920.SZ 002921.SZ 300684.SZ 002922.SZ 300735.SZ 603329.SH 603655.SH 603080.SH
0 16.30 17.71 4.58 2.88 14.60 2.62 4.96 4.66 5.37 6.02 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 17.02 19.20 4.65 3.02 15.97 2.65 4.95 4.70 5.37 6.27 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 17.02 17.28 4.56 3.06 14.37 2.63 4.82 4.47 5.37 5.96 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 16.18 16.97 4.49 2.95 13.10 2.73 4.89 4.33 5.37 5.77 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 16.95 17.19 4.55 2.99 13.18 2.77 4.97 4.42 5.37 5.92 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 3562 columns

# 写入hdf5文件
dayClose.to_hdf("test.h5",key="close")
pd.read_hdf("test.h5",key="close").head()
000001.SZ 000002.SZ 000004.SZ 000005.SZ 000006.SZ 000007.SZ 000008.SZ 000009.SZ 000010.SZ 000011.SZ ... 001965.SZ 603283.SH 002920.SZ 002921.SZ 300684.SZ 002922.SZ 300735.SZ 603329.SH 603655.SH 603080.SH
0 16.30 17.71 4.58 2.88 14.60 2.62 4.96 4.66 5.37 6.02 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 17.02 19.20 4.65 3.02 15.97 2.65 4.95 4.70 5.37 6.27 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 17.02 17.28 4.56 3.06 14.37 2.63 4.82 4.47 5.37 5.96 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 16.18 16.97 4.49 2.95 13.10 2.73 4.89 4.33 5.37 5.77 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 16.95 17.19 4.55 2.99 13.18 2.77 4.97 4.42 5.37 5.92 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 3562 columns

dayOpen = pd.read_hdf("day_open.h5")
dayOpen.to_hdf("test.h5",key="open")
pd.read_hdf("test.h5",key="open").head()
000001.SZ 000002.SZ 000004.SZ 000005.SZ 000006.SZ 000007.SZ 000008.SZ 000009.SZ 000010.SZ 000011.SZ ... 001965.SZ 603283.SH 002920.SZ 002921.SZ 300684.SZ 002922.SZ 300735.SZ 603329.SH 603655.SH 603080.SH
0 15.50 16.15 4.26 2.73 13.99 2.52 4.76 4.45 5.37 5.79 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 16.50 17.94 4.53 2.91 14.78 2.61 4.99 4.69 5.37 6.03 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 17.00 18.80 4.63 3.04 16.08 2.65 4.96 4.73 5.37 6.26 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 16.95 16.59 4.52 3.02 13.20 2.63 4.81 4.35 5.37 5.74 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 16.20 16.96 4.50 2.95 13.17 2.80 4.88 4.34 5.37 5.80 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 3562 columns

JSON格式
# 读取JSON格式文件
sa = pd.read_json("Sarcasm_Headlines_Dataset.json", orient="records", lines=True)
sa.head()
article_link headline is_sarcastic
0 https://www.huffingtonpost.com/entry/versace-b... former versace store clerk sues over secret 'b... 0
1 https://www.huffingtonpost.com/entry/roseanne-... the 'roseanne' revival catches up to our thorn... 0
2 https://local.theonion.com/mom-starting-to-fea... mom starting to fear son's web series closest ... 1
3 https://politics.theonion.com/boehner-just-wan... boehner just wants wife to listen, not come up... 1
4 https://www.huffingtonpost.com/entry/jk-rowlin... j.k. rowling wishes snape happy birthday in th... 0 
# 写入JSON格式文件
sa.to_json("test.json", orient="records", lines=True)
pd.read_json("test.json", orient="records", lines=True)
article_link headline is_sarcastic
0 https://www.huffingtonpost.com/entry/versace-b... former versace store clerk sues over secret 'b... 0
1 https://www.huffingtonpost.com/entry/roseanne-... the 'roseanne' revival catches up to our thorn... 0
2 https://local.theonion.com/mom-starting-to-fea... mom starting to fear son's web series closest ... 1
3 https://politics.theonion.com/boehner-just-wan... boehner just wants wife to listen, not come up... 1
4 https://www.huffingtonpost.com/entry/jk-rowlin... j.k. rowling wishes snape happy birthday in th... 0
... ... ... ...
26704 https://www.huffingtonpost.com/entry/american-... american politics in moral free-fall 0
26705 https://www.huffingtonpost.com/entry/americas-... america's best 20 hikes 0
26706 https://www.huffingtonpost.com/entry/reparatio... reparations and obama 0
26707 https://www.huffingtonpost.com/entry/israeli-b... israeli ban targeting boycott supporters raise... 0
26708 https://www.huffingtonpost.com/entry/gourmet-g... gourmet gifts for the foodie 2014 0

26709 rows × 3 columns

Pandas高级处理

缺失值处理
movie = pd.read_csv("IMDB-Movie-Data.csv")
movie.head()
Rank Title Genre Description Director Actors Year Runtime (Minutes) Rating Votes Revenue (Millions) Metascore
0 1 Guardians of the Galaxy Action,Adventure,Sci-Fi A group of intergalactic criminals are forced ... James Gunn Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... 2014 121 8.1 757074 333.13 76.0
1 2 Prometheus Adventure,Mystery,Sci-Fi Following clues to the origin of mankind, a te... Ridley Scott Noomi Rapace, Logan Marshall-Green, Michael Fa... 2012 124 7.0 485820 126.46 65.0
2 3 Split Horror,Thriller Three girls are kidnapped by a man with a diag... M. Night Shyamalan James McAvoy, Anya Taylor-Joy, Haley Lu Richar... 2016 117 7.3 157606 138.12 62.0
3 4 Sing Animation,Comedy,Family In a city of humanoid animals, a hustling thea... Christophe Lourdelet Matthew McConaughey,Reese Witherspoon, Seth Ma... 2016 108 7.2 60545 270.32 59.0
4 5 Suicide Squad Action,Adventure,Fantasy A secret government agency recruits some of th... David Ayer Will Smith, Jared Leto, Margot Robbie, Viola D... 2016 123 6.2 393727 325.02 40.0 
import numpy as np
# 判断是否存在缺失值
# 如果缺失值不是nan而是其他符号则先替换为nan再进行判断处理
# data_new = data.replace(to_replace="?", value=np.nan)
np.any(pd.isnull(movie)) # 返回True说明有缺失值

True

np.all(pd.notnull(movie)) # 返回False说明有缺失值

False

pd.notnull(movie).all() # 找到有缺失值的字段

Rank                   True
Title                  True
Genre                  True
Description            True
Director               True
Actors                 True
Year                   True
Runtime (Minutes)      True
Rating                 True
Votes                  True
Revenue (Millions)    False
Metascore             False
dtype: bool

# 删除有缺失值的样本
data1 = movie.dropna()
pd.notnull(data1).all()

Rank                  True
Title                 True
Genre                 True
Description           True
Director              True
Actors                True
Year                  True
Runtime (Minutes)     True
Rating                True
Votes                 True
Revenue (Millions)    True
Metascore             True
dtype: bool

# 替换有缺失值的样本
movie["Revenue (Millions)"].fillna(movie["Revenue (Millions)"].mean(), inplace=True)
movie["Metascore"].fillna(movie["Metascore"].mean(), inplace=True)
pd.notnull(movie).all() # 缺失值已经处理完毕

Rank                  True
Title                 True
Genre                 True
Description           True
Director              True
Actors                True
Year                  True
Runtime (Minutes)     True
Rating                True
Votes                 True
Revenue (Millions)    True
Metascore             True
dtype: bool
数据离散化
data = pd.read_csv("stock_day.csv")
p_change = data["p_change"]
# 自动分组
sr = pd.qcut(p_change,10)
sr.value_counts()

(-10.030999999999999, -4.836]    65
(-0.462, 0.26]                   65
(0.26, 0.94]                     65
(5.27, 10.03]                    65
(-4.836, -2.444]                 64
(-2.444, -1.352]                 64
(-1.352, -0.462]                 64
(1.738, 2.938]                   64
(2.938, 5.27]                    64
(0.94, 1.738]                    63
Name: p_change, dtype: int64

pd.get_dummies(sr, prefix="涨跌幅").head()
涨跌幅_(-10.030999999999999, -4.836] 涨跌幅_(-4.836, -2.444] 涨跌幅_(-2.444, -1.352] 涨跌幅_(-1.352, -0.462] 涨跌幅_(-0.462, 0.26] 涨跌幅_(0.26, 0.94] 涨跌幅_(0.94, 1.738] 涨跌幅_(1.738, 2.938] 涨跌幅_(2.938, 5.27] 涨跌幅_(5.27, 10.03]
2018-02-27 0 0 0 0 0 0 0 1 0 0
2018-02-26 0 0 0 0 0 0 0 0 1 0
2018-02-23 0 0 0 0 0 0 0 1 0 0
2018-02-22 0 0 0 0 0 0 1 0 0 0
2018-02-14 0 0 0 0 0 0 0 1 0 0 
# 自定义分组
bins = [-100, -7, -5, -3, 0, 3, 5, 7, 100]
sr = pd.cut(p_change, bins)
sr.value_counts()

(0, 3]        215
(-3, 0]       188
(3, 5]         57
(-5, -3]       51
(5, 7]         35
(7, 100]       35
(-100, -7]     34
(-7, -5]       28
Name: p_change, dtype: int64

data2 = pd.get_dummies(sr, prefix="rise")
data2.head()
rise_(-100, -7] rise_(-7, -5] rise_(-5, -3] rise_(-3, 0] rise_(0, 3] rise_(3, 5] rise_(5, 7] rise_(7, 100]
2018-02-27 0 0 0 0 1 0 0 0
2018-02-26 0 0 0 0 0 1 0 0
2018-02-23 0 0 0 0 1 0 0 0
2018-02-22 0 0 0 0 1 0 0 0
2018-02-14 0 0 0 0 1 0 0 0
合并
# 按方向拼接 pd.concat(data1,data2,axis = 0)
pd.concat([data, data2], axis=1).head()
open high close low volume price_change p_change ma5 ma10 ma20 ... v_ma20 turnover rise_(-100, -7] rise_(-7, -5] rise_(-5, -3] rise_(-3, 0] rise_(0, 3] rise_(3, 5] rise_(5, 7] rise_(7, 100]
2018-02-27 23.53 25.88 24.16 23.53 95578.03 0.63 2.68 22.942 22.142 22.875 ... 55576.11 2.39 0 0 0 0 1 0 0 0
2018-02-26 22.80 23.78 23.53 22.80 60985.11 0.69 3.02 22.406 21.955 22.942 ... 56007.50 1.53 0 0 0 0 0 1 0 0
2018-02-23 22.88 23.37 22.82 22.71 52914.01 0.54 2.42 21.938 21.929 23.022 ... 56372.85 1.32 0 0 0 0 1 0 0 0
2018-02-22 22.25 22.76 22.28 22.02 36105.01 0.36 1.64 21.446 21.909 23.137 ... 60149.60 0.90 0 0 0 0 1 0 0 0
2018-02-14 21.49 21.99 21.92 21.48 23331.04 0.44 2.05 21.366 21.923 23.253 ... 61716.11 0.58 0 0 0 0 1 0 0 0

5 rows × 22 columns

pd.concat([data, data2], axis=0).head()
open high close low volume price_change p_change ma5 ma10 ma20 ... v_ma20 turnover rise_(-100, -7] rise_(-7, -5] rise_(-5, -3] rise_(-3, 0] rise_(0, 3] rise_(3, 5] rise_(5, 7] rise_(7, 100]
2018-02-27 23.53 25.88 24.16 23.53 95578.03 0.63 2.68 22.942 22.142 22.875 ... 55576.11 2.39 NaN NaN NaN NaN NaN NaN NaN NaN
2018-02-26 22.80 23.78 23.53 22.80 60985.11 0.69 3.02 22.406 21.955 22.942 ... 56007.50 1.53 NaN NaN NaN NaN NaN NaN NaN NaN
2018-02-23 22.88 23.37 22.82 22.71 52914.01 0.54 2.42 21.938 21.929 23.022 ... 56372.85 1.32 NaN NaN NaN NaN NaN NaN NaN NaN
2018-02-22 22.25 22.76 22.28 22.02 36105.01 0.36 1.64 21.446 21.909 23.137 ... 60149.60 0.90 NaN NaN NaN NaN NaN NaN NaN NaN
2018-02-14 21.49 21.99 21.92 21.48 23331.04 0.44 2.05 21.366 21.923 23.253 ... 61716.11 0.58 NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 22 columns

pd.concat([data, data2], axis=0).tail()
open high close low volume price_change p_change ma5 ma10 ma20 ... v_ma20 turnover rise_(-100, -7] rise_(-7, -5] rise_(-5, -3] rise_(-3, 0] rise_(0, 3] rise_(3, 5] rise_(5, 7] rise_(7, 100]
2015-03-06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
2015-03-05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
2015-03-04 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
2015-03-03 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
2015-03-02 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0

5 rows × 22 columns

left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],'key2': ['K0', 'K1', 'K0', 'K1'],'A': ['A0', 'A1', 'A2', 'A3'],'B': ['B0', 'B1', 'B2', 'B3']})right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],'key2': ['K0', 'K0', 'K0', 'K0'],'C': ['C0', 'C1', 'C2', 'C3'],'D': ['D0', 'D1', 'D2', 'D3']})

left
key1 key2 A B
0 K0 K0 A0 B0
1 K0 K1 A1 B1
2 K1 K0 A2 B2
3 K2 K1 A3 B3 
right
key1 key2 C D
0 K0 K0 C0 D0
1 K1 K0 C1 D1
2 K1 K0 C2 D2
3 K2 K0 C3 D3 
pd.merge(left, right, how="inner", on=["key1", "key2"])
key1 key2 A B C D
0 K0 K0 A0 B0 C0 D0
1 K1 K0 A2 B2 C1 D1
2 K1 K0 A2 B2 C2 D2 
pd.merge(left, right, how="left", on=["key1", "key2"])
key1 key2 A B C D
0 K0 K0 A0 B0 C0 D0
1 K0 K1 A1 B1 NaN NaN
2 K1 K0 A2 B2 C1 D1
3 K1 K0 A2 B2 C2 D2
4 K2 K1 A3 B3 NaN NaN 
pd.merge(left, right, how="right", on=["key1", "key2"])
key1 key2 A B C D
0 K0 K0 A0 B0 C0 D0
1 K1 K0 A2 B2 C1 D1
2 K1 K0 A2 B2 C2 D2
3 K2 K0 NaN NaN C3 D3 
pd.merge(left, right, how="outer", on=["key1", "key2"])
key1 key2 A B C D
0 K0 K0 A0 B0 C0 D0
1 K0 K1 A1 B1 NaN NaN
2 K1 K0 A2 B2 C1 D1
3 K1 K0 A2 B2 C2 D2
4 K2 K1 A3 B3 NaN NaN
5 K2 K0 NaN NaN C3 D3 
# pd.crosstab(星期数据列, 涨跌幅数据列)
# 准备星期数据列
data.index

Index(['2018-02-27', '2018-02-26', '2018-02-23', '2018-02-22', '2018-02-14','2018-02-13', '2018-02-12', '2018-02-09', '2018-02-08', '2018-02-07',...'2015-03-13', '2015-03-12', '2015-03-11', '2015-03-10', '2015-03-09','2015-03-06', '2015-03-05', '2015-03-04', '2015-03-03', '2015-03-02'],dtype='object', length=643)

# pandas日期类型
date = pd.to_datetime(data.index)

date

DatetimeIndex(['2018-02-27', '2018-02-26', '2018-02-23', '2018-02-22','2018-02-14', '2018-02-13', '2018-02-12', '2018-02-09','2018-02-08', '2018-02-07',...'2015-03-13', '2015-03-12', '2015-03-11', '2015-03-10','2015-03-09', '2015-03-06', '2015-03-05', '2015-03-04','2015-03-03', '2015-03-02'],dtype='datetime64[ns]', length=643, freq=None)

data["week"] = date.weekday
date.weekday

Int64Index([1, 0, 4, 3, 2, 1, 0, 4, 3, 2,...4, 3, 2, 1, 0, 4, 3, 2, 1, 0],dtype='int64', length=643)

data.head()
open high close low volume price_change p_change ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 turnover week
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 1
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 0
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 4
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 3
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 2 
# 准备涨跌幅数据列
data["pona"] = np.where(data["p_change"] > 0, 1, 0)
data.head()
open high close low volume price_change p_change ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 turnover week pona
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 1 1
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 0 1
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 4 1
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 3 1
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 2 1
交叉表和透视表
# 交叉表
dataTable = pd.crosstab(data["week"], data["pona"])
dataTable
pona 0 1
week
0 63 62
1 55 76
2 61 71
3 63 65
4 59 68 
dataTable.sum(axis=1)

week
0    125
1    131
2    132
3    128
4    127
dtype: int64

dataTable.div(dataTable.sum(axis=1), axis=0).plot(kind="bar", stacked=True)

<AxesSubplot:xlabel='week'>

​

​

dataTable.div(dataTable.sum(axis=1), axis=0)
pona 0 1
week
0 0.504000 0.496000
1 0.419847 0.580153
2 0.462121 0.537879
3 0.492188 0.507812
4 0.464567 0.535433 
# 透视表
data.pivot_table(["pona"], index=["week"])
pona
week
0 0.496000
1 0.580153
2 0.537879
3 0.507812
4 0.535433
分组与聚合
col = pd.DataFrame({'color': ['white','red','green','red','green'], 'object': ['pen','pencil','pencil','ashtray','pen'],'price1':[5.56,4.20,1.30,0.56,2.75],'price2':[4.75,4.12,1.60,0.75,3.15]})
col
color object price1 price2
0 white pen 5.56 4.75
1 red pencil 4.20 4.12
2 green pencil 1.30 1.60
3 red ashtray 0.56 0.75
4 green pen 2.75 3.15 
# 进行分组，对颜色分组，price1进行聚合
# 用dataframe的方法进行分组
col.groupby(by="color")["price1"].max()

color
green    2.75
red      4.20
white    5.56
Name: price1, dtype: float64

col["price1"].groupby(col["color"]).max()

color
green    2.75
red      4.20
white    5.56
Name: price1, dtype: float64
实战案例 电影数据分析练习
数据文件：IMDB-Movie-Data.csv
问题1：我们想知道这些电影数据中评分的平均分，导演的人数等信息，我们应该怎么获取？
问题2：对于这一组电影数据，如果我们想rating，runtime的分布情况，应该如何呈现数据？
问题3：对于这一组电影数据，如果我们希望统计电影分类(genre)的情况，应该如何处理数据？

# 1、准备数据
movie = pd.read_csv("IMDB-Movie-Data.csv")
movie
Rank Title Genre Description Director Actors Year Runtime (Minutes) Rating Votes Revenue (Millions) Metascore
0 1 Guardians of the Galaxy Action,Adventure,Sci-Fi A group of intergalactic criminals are forced ... James Gunn Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... 2014 121 8.1 757074 333.13 76.0
1 2 Prometheus Adventure,Mystery,Sci-Fi Following clues to the origin of mankind, a te... Ridley Scott Noomi Rapace, Logan Marshall-Green, Michael Fa... 2012 124 7.0 485820 126.46 65.0
2 3 Split Horror,Thriller Three girls are kidnapped by a man with a diag... M. Night Shyamalan James McAvoy, Anya Taylor-Joy, Haley Lu Richar... 2016 117 7.3 157606 138.12 62.0
3 4 Sing Animation,Comedy,Family In a city of humanoid animals, a hustling thea... Christophe Lourdelet Matthew McConaughey,Reese Witherspoon, Seth Ma... 2016 108 7.2 60545 270.32 59.0
4 5 Suicide Squad Action,Adventure,Fantasy A secret government agency recruits some of th... David Ayer Will Smith, Jared Leto, Margot Robbie, Viola D... 2016 123 6.2 393727 325.02 40.0
... ... ... ... ... ... ... ... ... ... ... ... ...
995 996 Secret in Their Eyes Crime,Drama,Mystery A tight-knit team of rising investigators, alo... Billy Ray Chiwetel Ejiofor, Nicole Kidman, Julia Roberts... 2015 111 6.2 27585 NaN 45.0
996 997 Hostel: Part II Horror Three American college students studying abroa... Eli Roth Lauren German, Heather Matarazzo, Bijou Philli... 2007 94 5.5 73152 17.54 46.0
997 998 Step Up 2: The Streets Drama,Music,Romance Romantic sparks occur between two dance studen... Jon M. Chu Robert Hoffman, Briana Evigan, Cassie Ventura,... 2008 98 6.2 70699 58.01 50.0
998 999 Search Party Adventure,Comedy A pair of friends embark on a mission to reuni... Scot Armstrong Adam Pally, T.J. Miller, Thomas Middleditch,Sh... 2014 93 5.6 4881 NaN 22.0
999 1000 Nine Lives Comedy,Family,Fantasy A stuffy businessman finds himself trapped ins... Barry Sonnenfeld Kevin Spacey, Jennifer Garner, Robbie Amell,Ch... 2016 87 5.3 12435 19.64 11.0

1000 rows × 12 columns

# 问题1：我们想知道这些电影数据中评分的平均分，导演的人数等信息，我们应该怎么获取？
# 评分的平均分
movie["Rating"].mean()

6.723199999999999

# 导演的人数
np.unique(movie["Director"]).size

644

# 问题2：对于这一组电影数据，如果我们想rating，runtime的分布情况，应该如何呈现数据？
movie["Rating"].plot(kind="hist", figsize=(20, 8))

<AxesSubplot:ylabel='Frequency'>

​

​

import matplotlib.pyplot as plt
# 1、创建画布
plt.figure(figsize=(20, 8), dpi=80)# 2、绘制直方图
plt.hist(movie["Rating"], 20)# 修改刻度
plt.xticks(np.linspace(movie["Rating"].min(), movie["Rating"].max(), 21))# 添加网格
plt.grid(linestyle="--", alpha=0.5)# 3、显示图像
plt.show()

​

​

# 问题3：对于这一组电影数据，如果我们希望统计电影分类(genre)的情况，应该如何处理数据？# 1、创建画布
# 先统计电影类别都有哪些
movie_genre = [i.split(",") for i in movie["Genre"]]
movie_genre

[['Action', 'Adventure', 'Sci-Fi'],['Adventure', 'Mystery', 'Sci-Fi'],['Horror', 'Thriller'],['Animation', 'Comedy', 'Family'],['Action', 'Adventure', 'Fantasy'],['Action', 'Adventure', 'Fantasy'],['Comedy', 'Drama', 'Music'],['Comedy'],['Action', 'Adventure', 'Biography'],['Adventure', 'Drama', 'Romance'],['Adventure', 'Family', 'Fantasy'],['Biography', 'Drama', 'History'],['Action', 'Adventure', 'Sci-Fi'],['Animation', 'Adventure', 'Comedy'],['Action', 'Comedy', 'Drama'],['Animation', 'Adventure', 'Comedy'],['Biography', 'Drama', 'History'],['Action', 'Thriller'],['Biography', 'Drama'],['Drama', 'Mystery', 'Sci-Fi'],['Adventure', 'Drama', 'Thriller'],['Drama'],['Crime', 'Drama', 'Horror'],['Animation', 'Adventure', 'Comedy'],['Action', 'Adventure', 'Sci-Fi'],['Comedy'],['Action', 'Adventure', 'Drama'],['Horror', 'Thriller'],['Comedy'],['Action', 'Adventure', 'Drama'],['Comedy'],['Drama', 'Thriller'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Adventure', 'Comedy'],['Action', 'Horror', 'Sci-Fi'],['Action', 'Adventure', 'Sci-Fi'],['Adventure', 'Drama', 'Sci-Fi'],['Action', 'Adventure', 'Fantasy'],['Action', 'Adventure', 'Western'],['Comedy', 'Drama'],['Animation', 'Adventure', 'Comedy'],['Drama'],['Horror'],['Biography', 'Drama', 'History'],['Drama'],['Action', 'Adventure', 'Fantasy'],['Drama', 'Thriller'],['Adventure', 'Drama', 'Fantasy'],['Action', 'Adventure', 'Sci-Fi'],['Drama'],['Action', 'Adventure', 'Fantasy'],['Action', 'Adventure', 'Fantasy'],['Comedy', 'Drama'],['Action', 'Crime', 'Thriller'],['Action', 'Crime', 'Drama'],['Adventure', 'Drama', 'History'],['Crime', 'Horror', 'Thriller'],['Drama', 'Romance'],['Comedy', 'Drama', 'Romance'],['Biography', 'Drama'],['Action', 'Adventure', 'Sci-Fi'],['Horror', 'Mystery', 'Thriller'],['Crime', 'Drama', 'Mystery'],['Drama', 'Romance', 'Thriller'],['Drama', 'Mystery', 'Sci-Fi'],['Action', 'Adventure', 'Comedy'],['Drama', 'History', 'Thriller'],['Action', 'Adventure', 'Sci-Fi'],['Drama'],['Action', 'Drama', 'Thriller'],['Drama', 'History'],['Action', 'Drama', 'Romance'],['Drama', 'Fantasy'],['Drama', 'Romance'],['Animation', 'Adventure', 'Comedy'],['Action', 'Adventure', 'Fantasy'],['Action', 'Sci-Fi'],['Adventure', 'Drama', 'War'],['Action', 'Adventure', 'Fantasy'],['Action', 'Comedy', 'Fantasy'],['Action', 'Adventure', 'Sci-Fi'],['Comedy', 'Drama'],['Biography', 'Comedy', 'Crime'],['Crime', 'Drama', 'Mystery'],['Action', 'Crime', 'Thriller'],['Action', 'Adventure', 'Sci-Fi'],['Crime', 'Drama'],['Action', 'Adventure', 'Fantasy'],['Crime', 'Drama', 'Mystery'],['Action', 'Crime', 'Drama'],['Crime', 'Drama', 'Mystery'],['Action', 'Adventure', 'Fantasy'],['Drama'],['Comedy', 'Crime', 'Drama'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Comedy', 'Crime'],['Animation', 'Drama', 'Fantasy'],['Horror', 'Mystery', 'Sci-Fi'],['Drama', 'Mystery', 'Thriller'],['Crime', 'Drama', 'Thriller'],['Biography', 'Crime', 'Drama'],['Action', 'Adventure', 'Fantasy'],['Adventure', 'Drama', 'Sci-Fi'],['Crime', 'Mystery', 'Thriller'],['Action', 'Adventure', 'Comedy'],['Crime', 'Drama', 'Thriller'],['Comedy'],['Action', 'Adventure', 'Drama'],['Drama'],['Drama', 'Mystery', 'Sci-Fi'],['Action', 'Horror', 'Thriller'],['Biography', 'Drama', 'History'],['Romance', 'Sci-Fi'],['Action', 'Fantasy', 'War'],['Adventure', 'Drama', 'Fantasy'],['Comedy'],['Horror', 'Thriller'],['Action', 'Biography', 'Drama'],['Drama', 'Horror', 'Mystery'],['Animation', 'Adventure', 'Comedy'],['Adventure', 'Drama', 'Family'],['Adventure', 'Mystery', 'Sci-Fi'],['Adventure', 'Comedy', 'Romance'],['Action'],['Action', 'Thriller'],['Adventure', 'Drama', 'Family'],['Action', 'Adventure', 'Sci-Fi'],['Adventure', 'Crime', 'Mystery'],['Comedy', 'Family', 'Musical'],['Adventure', 'Drama', 'Thriller'],['Drama'],['Adventure', 'Comedy', 'Drama'],['Drama', 'Horror', 'Thriller'],['Drama', 'Music'],['Action', 'Crime', 'Thriller'],['Crime', 'Drama', 'Thriller'],['Crime', 'Drama', 'Thriller'],['Drama', 'Romance'],['Mystery', 'Thriller'],['Mystery', 'Thriller', 'Western'],['Action', 'Adventure', 'Sci-Fi'],['Comedy', 'Family'],['Biography', 'Comedy', 'Drama'],['Drama'],['Drama', 'Western'],['Drama', 'Mystery', 'Romance'],['Comedy', 'Drama'],['Action', 'Drama', 'Mystery'],['Comedy'],['Action', 'Adventure', 'Crime'],['Adventure', 'Family', 'Fantasy'],['Adventure', 'Sci-Fi', 'Thriller'],['Drama'],['Action', 'Crime', 'Drama'],['Drama', 'Horror', 'Mystery'],['Action', 'Horror', 'Sci-Fi'],['Action', 'Adventure', 'Sci-Fi'],['Comedy', 'Drama', 'Romance'],['Action', 'Comedy', 'Fantasy'],['Action', 'Comedy', 'Mystery'],['Thriller', 'War'],['Action', 'Comedy', 'Crime'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Adventure', 'Crime'],['Action', 'Adventure', 'Thriller'],['Drama', 'Fantasy', 'Romance'],['Action', 'Adventure', 'Comedy'],['Biography', 'Drama', 'History'],['Action', 'Drama', 'History'],['Action', 'Adventure', 'Thriller'],['Crime', 'Drama', 'Thriller'],['Animation', 'Adventure', 'Family'],['Adventure', 'Horror'],['Drama', 'Romance', 'Sci-Fi'],['Animation', 'Adventure', 'Comedy'],['Action', 'Adventure', 'Family'],['Action', 'Adventure', 'Drama'],['Action', 'Comedy'],['Horror', 'Mystery', 'Thriller'],['Action', 'Adventure', 'Comedy'],['Comedy', 'Romance'],['Horror', 'Mystery'],['Drama', 'Family', 'Fantasy'],['Sci-Fi'],['Drama', 'Thriller'],['Drama', 'Romance'],['Drama', 'War'],['Drama', 'Fantasy', 'Horror'],['Crime', 'Drama'],['Comedy', 'Drama', 'Romance'],['Drama', 'Romance'],['Drama'],['Crime', 'Drama', 'History'],['Horror', 'Sci-Fi', 'Thriller'],['Action', 'Drama', 'Sport'],['Action', 'Adventure', 'Sci-Fi'],['Crime', 'Drama', 'Thriller'],['Adventure', 'Biography', 'Drama'],['Biography', 'Drama', 'Thriller'],['Action', 'Comedy', 'Crime'],['Action', 'Adventure', 'Sci-Fi'],['Drama', 'Fantasy', 'Horror'],['Biography', 'Drama', 'Thriller'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Adventure', 'Mystery'],['Action', 'Adventure', 'Sci-Fi'],['Drama', 'Horror'],['Comedy', 'Drama', 'Romance'],['Comedy', 'Romance'],['Drama', 'Horror', 'Thriller'],['Action', 'Adventure', 'Drama'],['Drama'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Drama', 'Mystery'],['Action', 'Adventure', 'Fantasy'],['Action', 'Adventure', 'Fantasy'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Adventure', 'Comedy'],['Drama', 'Horror'],['Action', 'Comedy'],['Action', 'Adventure', 'Sci-Fi'],['Animation', 'Adventure', 'Comedy'],['Horror', 'Mystery'],['Crime', 'Drama', 'Mystery'],['Comedy', 'Crime'],['Drama'],['Comedy', 'Drama', 'Romance'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Adventure', 'Family'],['Horror', 'Sci-Fi', 'Thriller'],['Drama', 'Fantasy', 'War'],['Crime', 'Drama', 'Thriller'],['Action', 'Adventure', 'Drama'],['Action', 'Adventure', 'Thriller'],['Action', 'Adventure', 'Drama'],['Drama', 'Romance'],['Biography', 'Drama', 'History'],['Drama', 'Horror', 'Thriller'],['Adventure', 'Comedy', 'Drama'],['Action', 'Adventure', 'Romance'],['Action', 'Drama', 'War'],['Animation', 'Adventure', 'Comedy'],['Animation', 'Adventure', 'Comedy'],['Action', 'Adventure', 'Sci-Fi'],['Adventure', 'Family', 'Fantasy'],['Drama', 'Musical', 'Romance'],['Drama', 'Sci-Fi', 'Thriller'],['Comedy', 'Drama'],['Action', 'Comedy', 'Crime'],['Biography', 'Comedy', 'Drama'],['Comedy', 'Drama', 'Romance'],['Drama', 'Thriller'],['Biography', 'Drama', 'History'],['Action', 'Adventure', 'Sci-Fi'],['Horror', 'Mystery', 'Thriller'],['Comedy'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Drama', 'Sci-Fi'],['Horror'],['Drama', 'Thriller'],['Comedy', 'Drama', 'Romance'],['Drama', 'Thriller'],['Comedy', 'Drama'],['Drama'],['Action', 'Adventure', 'Comedy'],['Drama', 'Horror', 'Thriller'],['Comedy'],['Drama', 'Sci-Fi'],['Action', 'Adventure', 'Sci-Fi'],['Horror'],['Action', 'Adventure', 'Thriller'],['Adventure', 'Fantasy'],['Action', 'Comedy', 'Crime'],['Comedy', 'Drama', 'Music'],['Animation', 'Adventure', 'Comedy'],['Action', 'Adventure', 'Mystery'],['Action', 'Comedy', 'Crime'],['Crime', 'Drama', 'History'],['Comedy'],['Action', 'Adventure', 'Sci-Fi'],['Crime', 'Mystery', 'Thriller'],['Action', 'Adventure', 'Crime'],['Thriller'],['Biography', 'Drama', 'Romance'],['Action', 'Adventure'],['Action', 'Fantasy'],['Action', 'Comedy'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Comedy', 'Crime'],['Thriller'],['Action', 'Drama', 'Horror'],['Comedy', 'Music', 'Romance'],['Comedy'],['Drama'],['Action', 'Adventure', 'Fantasy'],['Drama', 'Romance'],['Animation', 'Adventure', 'Comedy'],['Comedy', 'Drama'],['Biography', 'Crime', 'Drama'],['Drama', 'History'],['Action', 'Crime', 'Thriller'],['Action', 'Biography', 'Drama'],['Horror'],['Comedy', 'Romance'],['Comedy', 'Romance'],['Comedy', 'Crime', 'Drama'],['Adventure', 'Family', 'Fantasy'],['Crime', 'Drama', 'Thriller'],['Action', 'Crime', 'Thriller'],['Comedy', 'Romance'],['Biography', 'Drama', 'Sport'],['Drama', 'Romance'],['Drama', 'Horror'],['Adventure', 'Fantasy'],['Adventure', 'Family', 'Fantasy'],['Action', 'Drama', 'Sci-Fi'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Horror'],['Comedy', 'Horror', 'Thriller'],['Action', 'Crime', 'Thriller'],['Crime', 'Drama', 'Music'],['Drama'],['Action', 'Crime', 'Thriller'],['Action', 'Sci-Fi', 'Thriller'],['Biography', 'Drama'],['Action', 'Adventure', 'Fantasy'],['Drama', 'Horror', 'Sci-Fi'],['Biography', 'Comedy', 'Drama'],['Crime', 'Horror', 'Thriller'],['Crime', 'Drama', 'Mystery'],['Animation', 'Adventure', 'Comedy'],['Action', 'Biography', 'Drama'],['Biography', 'Drama'],['Biography', 'Drama', 'History'],['Action', 'Biography', 'Drama'],['Drama', 'Fantasy', 'Horror'],['Comedy', 'Drama', 'Romance'],['Drama', 'Sport'],['Drama', 'Romance'],['Comedy', 'Romance'],['Action', 'Crime', 'Thriller'],['Action', 'Crime', 'Drama'],['Action', 'Drama', 'Thriller'],['Adventure', 'Family', 'Fantasy'],['Action', 'Adventure'],['Action', 'Adventure', 'Romance'],['Adventure', 'Family', 'Fantasy'],['Crime', 'Drama'],['Comedy', 'Horror'],['Comedy', 'Fantasy', 'Romance'],['Drama'],['Drama'],['Comedy', 'Drama'],['Comedy', 'Drama', 'Romance'],['Adventure', 'Sci-Fi', 'Thriller'],['Action', 'Adventure', 'Fantasy'],['Comedy', 'Drama'],['Biography', 'Drama', 'Romance'],['Comedy', 'Fantasy'],['Comedy', 'Drama', 'Fantasy'],['Comedy'],['Horror', 'Thriller'],['Action', 'Adventure', 'Sci-Fi'],['Adventure', 'Comedy', 'Horror'],['Comedy', 'Mystery'],['Drama'],['Adventure', 'Drama', 'Fantasy'],['Drama', 'Sport'],['Action', 'Adventure'],['Action', 'Adventure', 'Drama'],['Action', 'Drama', 'Sci-Fi'],['Action', 'Mystery', 'Sci-Fi'],['Action', 'Crime', 'Drama'],['Action', 'Crime', 'Fantasy'],['Biography', 'Comedy', 'Drama'],['Action', 'Crime', 'Thriller'],['Biography', 'Crime', 'Drama'],['Drama', 'Sport'],['Adventure', 'Comedy', 'Drama'],['Action', 'Adventure', 'Thriller'],['Comedy', 'Fantasy', 'Horror'],['Drama', 'Sport'],['Horror', 'Thriller'],['Drama', 'History', 'Thriller'],['Animation', 'Action', 'Adventure'],['Action', 'Adventure', 'Drama'],['Action', 'Comedy', 'Family'],['Action', 'Adventure', 'Drama'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Comedy'],['Action', 'Crime', 'Drama'],['Biography', 'Drama'],['Comedy', 'Romance'],['Comedy'],['Drama', 'Fantasy', 'Romance'],['Action', 'Adventure', 'Sci-Fi'],['Comedy'],['Comedy', 'Sci-Fi'],['Comedy', 'Drama'],['Animation', 'Action', 'Adventure'],['Horror'],['Action', 'Biography', 'Crime'],['Animation', 'Adventure', 'Comedy'],['Drama', 'Romance'],['Drama', 'Mystery', 'Thriller'],['Drama', 'History', 'Thriller'],['Animation', 'Adventure', 'Comedy'],['Action', 'Adventure', 'Sci-Fi'],['Adventure', 'Comedy'],['Action', 'Thriller'],['Comedy', 'Music'],['Animation', 'Adventure', 'Comedy'],['Crime', 'Drama', 'Thriller'],['Action', 'Adventure', 'Crime'],['Comedy', 'Drama', 'Horror'],['Drama'],['Drama', 'Mystery', 'Romance'],['Adventure', 'Family', 'Fantasy'],['Drama'],['Action', 'Drama', 'Thriller'],['Drama'],['Action', 'Horror', 'Romance'],['Action', 'Drama', 'Fantasy'],['Action', 'Crime', 'Drama'],['Drama', 'Fantasy', 'Romance'],['Action', 'Crime', 'Thriller'],['Action', 'Mystery', 'Thriller'],['Horror', 'Mystery', 'Thriller'],['Action', 'Horror', 'Sci-Fi'],['Comedy', 'Drama'],['Comedy'],['Action', 'Adventure', 'Horror'],['Action', 'Adventure', 'Thriller'],['Action', 'Crime', 'Drama'],['Comedy', 'Crime', 'Drama'],['Drama', 'Romance'],['Drama', 'Thriller'],['Action', 'Comedy', 'Crime'],['Comedy'],['Adventure', 'Family', 'Fantasy'],['Drama', 'Romance'],['Animation', 'Family', 'Fantasy'],['Drama', 'Romance'],['Thriller'],['Adventure', 'Horror', 'Mystery'],['Action', 'Sci-Fi'],['Adventure', 'Comedy', 'Drama'],['Animation', 'Action', 'Adventure'],['Drama', 'Horror'],['Action', 'Adventure', 'Sci-Fi'],['Comedy', 'Drama'],['Action', 'Horror', 'Mystery'],['Action', 'Thriller'],['Action', 'Adventure', 'Sci-Fi'],['Drama'],['Comedy', 'Drama', 'Romance'],['Comedy', 'Crime'],['Comedy', 'Romance'],['Drama', 'Romance'],['Crime', 'Drama', 'Thriller'],['Horror', 'Mystery', 'Thriller'],['Biography', 'Drama'],['Drama', 'Mystery', 'Sci-Fi'],['Adventure', 'Comedy', 'Family'],['Action', 'Adventure', 'Crime'],['Action', 'Crime', 'Mystery'],['Mystery', 'Thriller'],['Action', 'Sci-Fi', 'Thriller'],['Action', 'Comedy', 'Crime'],['Biography', 'Crime', 'Drama'],['Biography', 'Drama', 'History'],['Action', 'Adventure', 'Sci-Fi'],['Adventure', 'Family', 'Fantasy'],['Biography', 'Drama', 'History'],['Biography', 'Comedy', 'Drama'],['Drama', 'Thriller'],['Horror', 'Thriller'],['Drama'],['Drama', 'War'],['Comedy', 'Drama', 'Romance'],['Drama', 'Romance', 'Sci-Fi'],['Action', 'Crime', 'Drama'],['Comedy', 'Drama'],['Animation', 'Action', 'Adventure'],['Adventure', 'Comedy', 'Drama'],['Comedy', 'Drama', 'Family'],['Drama', 'Romance', 'Thriller'],['Comedy', 'Crime', 'Drama'],['Animation', 'Comedy', 'Family'],['Drama', 'Horror', 'Sci-Fi'],['Action', 'Adventure', 'Drama'],['Action', 'Horror', 'Sci-Fi'],['Action', 'Crime', 'Sport'],['Drama', 'Horror', 'Sci-Fi'],['Drama', 'Horror', 'Sci-Fi'],['Action', 'Adventure', 'Comedy'],['Mystery', 'Sci-Fi', 'Thriller'],['Crime', 'Drama', 'Thriller'],['Animation', 'Adventure', 'Comedy'],['Action', 'Sci-Fi', 'Thriller'],['Drama', 'Romance'],['Crime', 'Drama', 'Thriller'],['Comedy', 'Drama', 'Music'],['Drama', 'Fantasy', 'Romance'],['Crime', 'Drama', 'Thriller'],['Crime', 'Drama', 'Thriller'],['Comedy', 'Drama', 'Romance'],['Comedy', 'Romance'],['Drama', 'Sci-Fi', 'Thriller'],['Drama', 'War'],['Action', 'Crime', 'Drama'],['Sci-Fi', 'Thriller'],['Adventure', 'Drama', 'Horror'],['Comedy', 'Drama', 'Music'],['Comedy', 'Drama', 'Romance'],['Action', 'Adventure', 'Drama'],['Action', 'Crime', 'Drama'],['Adventure', 'Fantasy'],['Drama', 'Romance'],['Biography', 'History', 'Thriller'],['Crime', 'Drama', 'Thriller'],['Action', 'Drama', 'History'],['Biography', 'Comedy', 'Drama'],['Crime', 'Drama', 'Thriller'],['Action', 'Biography', 'Drama'],['Action', 'Drama', 'Sci-Fi'],['Adventure', 'Horror'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Adventure', 'Mystery'],['Comedy', 'Drama', 'Romance'],['Horror', 'Thriller'],['Action', 'Sci-Fi', 'Thriller'],['Action', 'Sci-Fi', 'Thriller'],['Biography', 'Drama'],['Action', 'Crime', 'Drama'],['Action', 'Crime', 'Mystery'],['Action', 'Adventure', 'Comedy'],['Crime', 'Drama', 'Thriller'],['Crime', 'Drama'],['Mystery', 'Thriller'],['Mystery', 'Sci-Fi', 'Thriller'],['Action', 'Mystery', 'Sci-Fi'],['Drama', 'Romance'],['Drama', 'Thriller'],['Drama', 'Mystery', 'Sci-Fi'],['Comedy', 'Drama'],['Adventure', 'Family', 'Fantasy'],['Biography', 'Drama', 'Sport'],['Drama'],['Comedy', 'Drama', 'Romance'],['Biography', 'Drama', 'Romance'],['Action', 'Adventure', 'Sci-Fi'],['Drama', 'Sci-Fi', 'Thriller'],['Drama', 'Romance', 'Thriller'],['Mystery', 'Thriller'],['Mystery', 'Thriller'],['Action', 'Drama', 'Fantasy'],['Action', 'Adventure', 'Biography'],['Adventure', 'Comedy', 'Sci-Fi'],['Action', 'Adventure', 'Thriller'],['Fantasy', 'Horror'],['Horror', 'Mystery'],['Animation', 'Adventure', 'Comedy'],['Action', 'Adventure', 'Drama'],['Adventure', 'Family', 'Fantasy'],['Action', 'Adventure', 'Sci-Fi'],['Comedy', 'Drama'],['Comedy', 'Drama'],['Crime', 'Drama', 'Thriller'],['Comedy', 'Romance'],['Animation', 'Comedy', 'Family'],['Comedy', 'Drama'],['Comedy', 'Drama'],['Biography', 'Drama', 'Sport'],['Action', 'Adventure', 'Fantasy'],['Action', 'Drama', 'History'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Adventure', 'Mystery'],['Crime', 'Drama', 'Mystery'],['Action'],['Action', 'Adventure', 'Family'],['Comedy', 'Romance'],['Comedy', 'Drama', 'Romance'],['Biography', 'Drama', 'Sport'],['Action', 'Fantasy', 'Thriller'],['Biography', 'Drama', 'Sport'],['Action', 'Drama', 'Fantasy'],['Adventure', 'Sci-Fi', 'Thriller'],['Animation', 'Adventure', 'Comedy'],['Drama', 'Mystery', 'Thriller'],['Drama', 'Romance'],['Crime', 'Drama', 'Mystery'],['Comedy', 'Romance', 'Sport'],['Comedy', 'Family'],['Drama', 'Horror', 'Mystery'],['Action', 'Drama', 'Sport'],['Action', 'Adventure', 'Comedy'],['Drama', 'Mystery', 'Sci-Fi'],['Animation', 'Action', 'Comedy'],['Action', 'Crime', 'Drama'],['Action', 'Crime', 'Drama'],['Comedy', 'Drama', 'Romance'],['Animation', 'Action', 'Adventure'],['Crime', 'Drama'],['Drama'],['Drama'],['Comedy', 'Crime'],['Drama'],['Action', 'Adventure', 'Fantasy'],['Drama', 'Fantasy', 'Romance'],['Comedy', 'Drama'],['Drama', 'Fantasy', 'Thriller'],['Biography', 'Crime', 'Drama'],['Comedy', 'Drama', 'Romance'],['Action', 'Crime', 'Drama'],['Sci-Fi'],['Action', 'Biography', 'Drama'],['Action', 'Comedy', 'Romance'],['Adventure', 'Comedy', 'Drama'],['Comedy', 'Crime', 'Drama'],['Action', 'Fantasy', 'Horror'],['Drama', 'Horror'],['Horror'],['Action', 'Thriller'],['Action', 'Adventure', 'Mystery'],['Action', 'Adventure', 'Fantasy'],['Comedy', 'Drama', 'Romance'],['Crime', 'Drama', 'Mystery'],['Adventure', 'Comedy', 'Family'],['Comedy', 'Drama', 'Romance'],['Comedy'],['Comedy', 'Drama', 'Horror'],['Drama', 'Horror', 'Thriller'],['Animation', 'Adventure', 'Family'],['Comedy', 'Romance'],['Mystery', 'Romance', 'Sci-Fi'],['Crime', 'Drama'],['Drama', 'Horror', 'Mystery'],['Comedy'],['Biography', 'Drama'],['Comedy', 'Drama', 'Thriller'],['Comedy', 'Western'],['Drama', 'History', 'War'],['Drama', 'Horror', 'Sci-Fi'],['Drama'],['Comedy', 'Drama'],['Fantasy', 'Horror', 'Thriller'],['Drama', 'Romance'],['Action', 'Comedy', 'Fantasy'],['Drama', 'Horror', 'Musical'],['Crime', 'Drama', 'Mystery'],['Horror', 'Mystery', 'Thriller'],['Comedy', 'Music'],['Drama'],['Biography', 'Crime', 'Drama'],['Drama'],['Action', 'Adventure', 'Comedy'],['Crime', 'Drama', 'Mystery'],['Drama'],['Action', 'Comedy', 'Crime'],['Comedy', 'Drama', 'Romance'],['Crime', 'Drama', 'Mystery'],['Action', 'Comedy', 'Crime'],['Drama'],['Drama', 'Romance'],['Crime', 'Drama', 'Mystery'],['Adventure', 'Comedy', 'Romance'],['Comedy', 'Crime', 'Drama'],['Adventure', 'Drama', 'Thriller'],['Biography', 'Crime', 'Drama'],['Crime', 'Drama', 'Thriller'],['Drama', 'History', 'Thriller'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Comedy'],['Horror'],['Action', 'Crime', 'Mystery'],['Comedy', 'Romance'],['Comedy'],['Action', 'Drama', 'Thriller'],['Action', 'Adventure', 'Sci-Fi'],['Drama', 'Mystery', 'Thriller'],['Comedy', 'Drama', 'Romance'],['Action', 'Fantasy', 'Horror'],['Drama', 'Romance'],['Biography', 'Drama'],['Biography', 'Drama'],['Action', 'Adventure', 'Sci-Fi'],['Animation', 'Adventure', 'Comedy'],['Drama', 'Mystery', 'Thriller'],['Action', 'Horror', 'Sci-Fi'],['Drama', 'Romance'],['Biography', 'Drama'],['Action', 'Adventure', 'Drama'],['Adventure', 'Drama', 'Fantasy'],['Drama', 'Family'],['Comedy', 'Drama', 'Romance'],['Drama', 'Romance', 'Sci-Fi'],['Action', 'Adventure', 'Thriller'],['Comedy', 'Romance'],['Crime', 'Drama', 'Horror'],['Comedy', 'Fantasy'],['Action', 'Comedy', 'Crime'],['Adventure', 'Drama', 'Romance'],['Action', 'Crime', 'Drama'],['Crime', 'Horror', 'Thriller'],['Romance', 'Sci-Fi', 'Thriller'],['Comedy', 'Drama', 'Romance'],['Crime', 'Drama'],['Crime', 'Drama', 'Mystery'],['Action', 'Adventure', 'Sci-Fi'],['Animation', 'Fantasy'],['Animation', 'Adventure', 'Comedy'],['Drama', 'Mystery', 'War'],['Comedy', 'Romance'],['Animation', 'Comedy', 'Family'],['Comedy'],['Horror', 'Mystery', 'Thriller'],['Action', 'Adventure', 'Drama'],['Comedy'],['Drama'],['Adventure', 'Biography', 'Drama'],['Comedy'],['Horror', 'Thriller'],['Action', 'Drama', 'Family'],['Comedy', 'Fantasy', 'Horror'],['Comedy', 'Romance'],['Drama', 'Mystery', 'Romance'],['Action', 'Adventure', 'Comedy'],['Thriller'],['Comedy'],['Adventure', 'Comedy', 'Sci-Fi'],['Comedy', 'Drama', 'Fantasy'],['Mystery', 'Thriller'],['Comedy', 'Drama'],['Adventure', 'Drama', 'Family'],['Horror', 'Thriller'],['Action', 'Drama', 'Romance'],['Drama', 'Romance'],['Action', 'Adventure', 'Fantasy'],['Comedy'],['Action', 'Biography', 'Drama'],['Drama', 'Mystery', 'Romance'],['Adventure', 'Drama', 'Western'],['Drama', 'Music', 'Romance'],['Comedy', 'Romance', 'Western'],['Thriller'],['Comedy', 'Drama', 'Romance'],['Horror', 'Thriller'],['Adventure', 'Family', 'Fantasy'],['Crime', 'Drama', 'Mystery'],['Horror', 'Mystery'],['Comedy', 'Crime', 'Drama'],['Action', 'Comedy', 'Romance'],['Biography', 'Drama', 'History'],['Adventure', 'Drama'],['Drama', 'Thriller'],['Drama'],['Action', 'Adventure', 'Fantasy'],['Action', 'Biography', 'Drama'],['Drama', 'Music'],['Comedy', 'Drama'],['Drama', 'Thriller', 'War'],['Action', 'Mystery', 'Thriller'],['Horror', 'Sci-Fi', 'Thriller'],['Comedy', 'Drama', 'Romance'],['Action', 'Sci-Fi'],['Action', 'Adventure', 'Fantasy'],['Drama', 'Mystery', 'Romance'],['Drama'],['Action', 'Adventure', 'Thriller'],['Action', 'Crime', 'Thriller'],['Animation', 'Action', 'Adventure'],['Drama', 'Fantasy', 'Mystery'],['Drama', 'Sci-Fi'],['Animation', 'Adventure', 'Comedy'],['Horror', 'Thriller'],['Action', 'Thriller'],['Comedy'],['Biography', 'Drama'],['Action', 'Mystery', 'Thriller'],['Action', 'Mystery', 'Sci-Fi'],['Crime', 'Drama', 'Thriller'],['Comedy', 'Romance'],['Comedy', 'Drama', 'Romance'],['Biography', 'Drama', 'Thriller'],['Drama'],['Action', 'Adventure', 'Family'],['Animation', 'Comedy', 'Family'],['Action', 'Crime', 'Drama'],['Comedy'],['Comedy', 'Crime', 'Thriller'],['Comedy', 'Romance'],['Animation', 'Comedy', 'Drama'],['Action', 'Crime', 'Thriller'],['Comedy', 'Romance'],['Adventure', 'Biography', 'Drama'],['Animation', 'Adventure', 'Comedy'],['Crime', 'Drama', 'Mystery'],['Action', 'Comedy', 'Sci-Fi'],['Comedy', 'Fantasy', 'Horror'],['Comedy', 'Crime'],['Animation', 'Action', 'Adventure'],['Action', 'Drama', 'Thriller'],['Fantasy', 'Horror'],['Crime', 'Drama', 'Thriller'],['Action', 'Adventure', 'Fantasy'],['Comedy', 'Drama', 'Romance'],['Biography', 'Drama', 'Romance'],['Action', 'Drama', 'History'],['Action', 'Adventure', 'Comedy'],['Horror', 'Thriller'],['Horror', 'Mystery', 'Thriller'],['Comedy', 'Romance'],['Animation', 'Adventure', 'Comedy'],['Crime', 'Drama', 'Mystery'],['Crime', 'Drama', 'Mystery'],['Adventure', 'Biography', 'Drama'],['Horror', 'Mystery', 'Thriller'],['Horror', 'Thriller'],['Drama', 'Romance', 'War'],['Adventure', 'Fantasy', 'Mystery'],['Action', 'Adventure', 'Sci-Fi'],['Biography', 'Drama'],['Drama', 'Thriller'],['Horror', 'Thriller'],['Drama', 'Horror', 'Thriller'],['Action', 'Adventure', 'Fantasy'],['Action', 'Horror', 'Thriller'],['Comedy'],['Drama', 'Sport'],['Comedy', 'Family'],['Drama', 'Romance'],['Action', 'Adventure', 'Comedy'],['Comedy'],['Mystery', 'Romance', 'Thriller'],['Crime', 'Drama'],['Action', 'Comedy'],['Crime', 'Drama', 'Mystery'],['Biography', 'Drama', 'Romance'],['Comedy', 'Crime'],['Drama', 'Thriller'],['Drama'],['Animation', 'Adventure', 'Comedy'],['Action', 'Thriller'],['Drama', 'Thriller'],['Animation', 'Adventure', 'Comedy'],['Crime', 'Drama', 'Mystery'],['Thriller'],['Biography', 'Drama', 'Sport'],['Crime', 'Drama', 'Thriller'],['Drama', 'Music'],['Crime', 'Drama', 'Thriller'],['Drama', 'Romance'],['Animation', 'Action', 'Adventure'],['Comedy', 'Drama'],['Action', 'Adventure', 'Drama'],['Biography', 'Crime', 'Drama'],['Horror'],['Biography', 'Drama', 'Mystery'],['Drama', 'Romance'],['Animation', 'Drama', 'Romance'],['Comedy', 'Family'],['Drama'],['Mystery', 'Thriller'],['Drama', 'Fantasy', 'Horror'],['Drama', 'Romance'],['Biography', 'Drama', 'History'],['Comedy', 'Family'],['Action', 'Adventure', 'Thriller'],['Comedy', 'Drama'],['Action', 'Adventure', 'Fantasy'],['Action', 'Thriller'],['Drama', 'Romance'],['Comedy', 'Drama', 'Romance'],['Drama', 'Horror', 'Sci-Fi'],['Comedy', 'Horror', 'Romance'],['Drama'],['Action', 'Adventure', 'Sci-Fi'],['Action', 'Adventure', 'Fantasy'],['Action', 'Adventure', 'Drama'],['Biography', 'Comedy', 'Drama'],['Drama', 'Mystery', 'Romance'],['Animation', 'Adventure', 'Comedy'],['Drama', 'Romance', 'Sci-Fi'],['Drama'],['Drama', 'Fantasy'],['Drama', 'Romance'],['Comedy', 'Horror', 'Thriller'],['Comedy', 'Drama', 'Romance'],['Crime', 'Drama'],['Comedy', 'Romance'],['Action', 'Drama', 'Family'],['Comedy', 'Drama', 'Romance'],['Action', 'Thriller', 'War'],['Action', 'Comedy', 'Horror'],['Biography', 'Drama', 'Sport'],['Adventure', 'Comedy', 'Drama'],['Comedy', 'Romance'],['Comedy', 'Romance'],['Comedy', 'Drama', 'Romance'],['Action', 'Adventure', 'Crime'],['Comedy', 'Romance'],['Animation', 'Action', 'Adventure'],['Action', 'Crime', 'Sci-Fi'],['Drama'],['Comedy', 'Drama', 'Romance'],['Crime', 'Thriller'],['Comedy', 'Horror', 'Sci-Fi'],['Drama', 'Thriller'],['Drama', 'Fantasy', 'Horror'],['Thriller'],['Adventure', 'Drama', 'Family'],['Mystery', 'Sci-Fi', 'Thriller'],['Biography', 'Crime', 'Drama'],['Drama', 'Fantasy', 'Horror'],['Action', 'Adventure', 'Thriller'],['Crime', 'Drama', 'Horror'],['Crime', 'Drama', 'Fantasy'],['Adventure', 'Family', 'Fantasy'],['Action', 'Adventure', 'Drama'],['Action', 'Comedy', 'Horror'],['Comedy', 'Drama', 'Family'],['Action', 'Thriller'],['Action', 'Adventure', 'Sci-Fi'],['Adventure', 'Drama', 'Fantasy'],['Drama'],['Drama'],['Comedy'],['Drama'],['Comedy', 'Drama', 'Music'],['Drama', 'Fantasy', 'Music'],['Drama'],['Thriller'],['Comedy', 'Horror'],['Action', 'Comedy', 'Sport'],['Horror'],['Comedy', 'Drama'],['Action', 'Drama', 'Thriller'],['Drama', 'Romance'],['Horror', 'Mystery'],['Adventure', 'Drama', 'Fantasy'],['Thriller'],['Comedy', 'Romance'],['Action', 'Sci-Fi', 'Thriller'],['Fantasy', 'Mystery', 'Thriller'],['Biography', 'Drama'],['Crime', 'Drama'],['Action', 'Adventure', 'Sci-Fi'],['Adventure'],['Comedy', 'Drama'],['Comedy', 'Drama'],['Comedy', 'Drama', 'Romance'],['Adventure', 'Comedy', 'Drama'],['Action', 'Sci-Fi', 'Thriller'],['Comedy', 'Romance'],['Action', 'Fantasy', 'Horror'],['Crime', 'Drama', 'Thriller'],['Action', 'Drama', 'Thriller'],['Crime', 'Drama', 'Mystery'],['Crime', 'Drama', 'Mystery'],['Drama', 'Sci-Fi', 'Thriller'],['Biography', 'Drama', 'History'],['Crime', 'Horror', 'Thriller'],['Drama'],['Drama', 'Mystery', 'Thriller'],['Adventure', 'Biography'],['Adventure', 'Biography', 'Crime'],['Action', 'Horror', 'Thriller'],['Action', 'Adventure', 'Western'],['Horror', 'Thriller'],['Drama', 'Mystery', 'Thriller'],['Comedy', 'Drama', 'Musical'],['Horror', 'Mystery'],['Biography', 'Drama', 'Sport'],['Comedy', 'Family', 'Romance'],['Drama', 'Mystery', 'Thriller'],['Comedy'],['Drama'],['Drama', 'Thriller'],['Biography', 'Drama', 'Family'],['Comedy', 'Drama', 'Family'],['Drama', 'Fantasy', 'Musical'],['Comedy'],['Adventure', 'Family'],['Adventure', 'Comedy', 'Fantasy'],['Horror', 'Thriller'],['Drama', 'Romance'],['Horror'],['Biography', 'Drama', 'History'],['Action', 'Adventure', 'Fantasy'],['Drama', 'Family', 'Music'],['Comedy', 'Drama', 'Romance'],['Action', 'Adventure', 'Horror'],['Comedy'],['Crime', 'Drama', 'Mystery'],['Horror'],['Drama', 'Music', 'Romance'],['Adventure', 'Comedy'],['Comedy', 'Family', 'Fantasy']]

genres = np.unique([j for i in movie_genre for j in i])
genres

array(['Action', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime','Drama', 'Family', 'Fantasy', 'History', 'Horror', 'Music','Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Sport', 'Thriller','War', 'Western'], dtype='<U9')

# 每个类别有几部电影
count = pd.DataFrame(np.zeros(shape=[1000,20],dtype="int32"),columns=genres)
count.head()
Action Adventure Animation Biography Comedy Crime Drama Family Fantasy History Horror Music Musical Mystery Romance Sci-Fi Sport Thriller War Western
0 0 0 0 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 0 0 0 0
2 0 0 0 0 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 0 0 0 0
4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 
# 计数填表
for i in range(1000):count.loc[i, movie_genre[i]] = 1
count
Action Adventure Animation Biography Comedy Crime Drama Family Fantasy History Horror Music Musical Mystery Romance Sci-Fi Sport Thriller War Western
0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0
3 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
4 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
995 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0
996 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
997 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0
998 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
999 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0

1000 rows × 20 columns

count.sum(axis=0).sort_values(ascending=False).plot(kind="pie",figsize=(10,10),fontsize=20)

<AxesSubplot:ylabel='None'>

​

​

count.sum(axis=0).sort_values(ascending=False).plot(kind="bar",figsize=(20,9),fontsize=40,colormap="cool")

<AxesSubplot:>

​

​

Pandas 学习笔记二相关推荐

  1. pandas学习笔记二之pandas选择器

    pandas选择器

  2. Python科学计算-Numpy和Pandas学习笔记(二)-Numpy的基本运算(chaochao`s Blog)

    1 前言 本章介绍numpy的基本运算. 2 Numpy的基本运算 numpy的基本运算是对矩阵中每个元素进行相应的运算. 2.1 加减乘除 加.减.乘.除. import numpy as np a ...

  3. pandas学习笔记：pandas.Dataframe.rename()函数用法

    pandas学习笔记:pandas.Dataframe.rename()函数用法 pandas.Dataframe.rename()函数主要是用来修改Dataframe数据的行名和列名. 主要用到的参 ...

  4. 深度强化学习笔记(二)——Q-learning学习与二维寻路demo实现

    深度强化学习笔记(二)--Q-learning学习与二维寻路demo实现 文章目录 深度强化学习笔记(二)--Q-learning学习与二维寻路demo实现 前言 理论 什么是Q-Learning 算 ...

  5. pandas学习笔记之DateFrame

    pandas学习笔记之DateFrame 文章目录 pandas学习笔记之DateFrame 1.DateFrame的创建 1)认识DataFrame对象 2)由二维列表创建(默认index和colu ...

  6. 数据分析之pandas学习笔记（六）（层次化索引、重塑、轴向旋转、行列变换、合并表数据）

    数据分析之Pandas学习笔记(六)(层次化索引.重塑.轴向旋转.行列变换.合并表数据) level层次化索引 unstack()与stack()进行重塑,即:行列索引变换 swaplevel()交换 ...

  7. Pandas学习（二）——双色球开奖数据分析

    学习笔记汇总 Pandas学习(一)–数据的导入 pandas学习(二)–双色球数据分析 pandas学习(三)–NAB球员薪资分析 pandas学习(四)–数据的归一化 pandas学习(五)–pa ...

  8. [Pandas 学习笔记] - No.1 pandas学习笔记

    pandas学习笔记 pandas是基于numpy开发出的数据分析包,用于高效地操作大型数据集.pandas的数据结构有三种 分别为 series,dataframe和panel,对应一维,二维,三维 ...

  9. qml学习笔记(二)：可视化元素基类Item详解（上半场anchors等等）

    原博主博客地址:http://blog.csdn.net/qq21497936 本文章博客地址:http://blog.csdn.net/qq21497936/article/details/7851 ...

  10. [转载]dorado学习笔记(二)

    原文地址:dorado学习笔记(二)作者:傻掛 ·isFirst, isLast在什么情况下使用?在遍历dataset的时候会用到 ·dorado执行的顺序,首先由jsp发送请求,调用相关的ViewM ...

最新文章

  1. FastAPI 自动生成的docs文档没法使用
  2. logistic 损失函数的解释
  3. zabbix web前端取值同后端取值不一致
  4. 借花献佛！成都java工程师培训
  5. GIt版本回退还不会用？轻松学会不怕失误
  6. Java 8 Friday：使用Streams API时的10个细微错误
  7. 在c语言中,以r方式不能打开并不存在的文件,C语言文件选择题
  8. 能利用计算机来模拟某种真实的实验现象,自然现象或社会现象的课件是,幼儿园课件制作教案...
  9. 网络恶意营销账号猖獗，国家网信办出手了
  10. html知识管理,index.html
  11. 阿里云云计算6 ECS的概念
  12. 严把质量关、做好可靠性与环境试验，不做被车压塌的桥！
  13. SOLA（苏拉）病毒
  14. 平稳时间序列模型的统计性质
  15. 当前常见游戏服务器引擎
  16. 层次分析法（AHP）原理以及应用
  17. 计算机usb显示不出来怎么办,U盘在电脑上显示不出来怎么办
  18. Taro 与 React 的差异（Taro的约束(keng)）
  19. 移动端（一）—— 移动端概念
  20. 微信公众号推文发布方法（内涵详细步骤）

热门文章

  1. xp系统怎么查看计算机共享文件夹,xp系统提升查看局域网共享文件速度方法分享...
  2. 蓝桥杯算法训练——调和数列问题
  3. 【Ubuntu】Ubuntu18.04主题，图标，登陆美化
  4. linux木马查杀工具,【Kali】linux木马查杀
  5. (三) SiamRPN从论文角度介绍
  6. 女生转行学习IT技术需要考虑什么
  7. 最大公约数与最小公倍数的求法
  8. 专科学历去学计算机，前景到底如何？
  9. 计算机图形图像处理应用教程,计算机图形图像处理应用教程
  10. HTTP Live Streaming (HLS) 协议科普扫盲