Pandas 学习笔记二

2023年7月17日下午7:11 • 人工智能 • 阅读 35

文章目录

*
–
+ Pandas 学习笔记二
+
* 数据的读取与存储
*
– csv格式
– hdf5格式
– JSON格式
* Pandas高级处理
*
– 缺失值处理
– 数据离散化
– 合并
– 交叉表和透视表
– 分组与聚合
– 实战案例电影数据分析练习

Pandas 学习笔记二

数据的读取与存储

csv格式

import pandas as pd

data = pd.read_csv("stock_day.csv",usecols=['open','high','low','close'])
data.head()

openhighcloselow2018-02-2723.5325.8824.1623.532018-02-2622.8023.7823.5322.802018-02-2322.8823.3722.8222.712018-02-2222.2522.7622.2822.022018-02-1421.4921.9921.9221.48

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()

openhighcloselowvolumeprice_changep_changema5ma10ma20v_ma5v_ma10v_ma20turnover2018-02-2723.5325.8824.1623.5395578.030.632.6822.94222.14222.87553782.6446738.6555576.112.392018-02-2622.8023.7823.5322.8060985.110.693.0222.40621.95522.94240827.5242736.3456007.501.532018-02-2322.8823.3722.8222.7152914.010.542.4221.93821.92923.02235119.5841871.9756372.851.322018-02-2222.2522.7622.2822.0236105.010.361.6421.44621.90923.13735397.5839904.7860149.600.902018-02-1421.4921.9921.9221.4823331.040.442.0521.36621.92323.25333590.2142935.7461716.110.58


data[:10].to_csv("test.csv", columns=["open"])
pd.read_csv("test.csv")

Unnamed: 0open02018-02-2723.5312018-02-2622.8022018-02-2322.8832018-02-2222.2542018-02-1421.4952018-02-1321.4062018-02-1220.7072018-02-0921.2082018-02-0821.7992018-02-0722.69

data[:10].to_csv("test.csv", columns=["open"], index=False, mode="a", header=False)
pd.read_csv("test.csv")

Unnamed: 0open02018-02-2723.5312018-02-2622.8022018-02-2322.8832018-02-2222.2542018-02-1421.4952018-02-1321.4062018-02-1220.7072018-02-0921.2082018-02-0821.7992018-02-0722.691023.53NaN1122.8NaN1222.88NaN1322.25NaN1421.49NaN1521.4NaN1620.7NaN1721.2NaN1821.79NaN1922.69NaN

hdf5格式


dayClose = pd.read_hdf("day_close.h5")
dayClose.head()

000001.SZ000002.SZ000004.SZ000005.SZ000006.SZ000007.SZ000008.SZ000009.SZ000010.SZ000011.SZ…001965.SZ603283.SH002920.SZ002921.SZ300684.SZ002922.SZ300735.SZ603329.SH603655.SH603080.SH016.3017.714.582.8814.602.624.964.665.376.02…NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN117.0219.204.653.0215.972.654.954.705.376.27…NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN217.0217.284.563.0614.372.634.824.475.375.96…NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN316.1816.974.492.9513.102.734.894.335.375.77…NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN416.9517.194.552.9913.182.774.974.425.375.92…NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

5 rows × 3562 columns


dayClose.to_hdf("test.h5",key="close")
pd.read_hdf("test.h5",key="close").head()

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.SZ000002.SZ000004.SZ000005.SZ000006.SZ000007.SZ000008.SZ000009.SZ000010.SZ000011.SZ…001965.SZ603283.SH002920.SZ002921.SZ300684.SZ002922.SZ300735.SZ603329.SH603655.SH603080.SH015.5016.154.262.7313.992.524.764.455.375.79…NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN116.5017.944.532.9114.782.614.994.695.376.03…NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN217.0018.804.633.0416.082.654.964.735.376.26…NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN316.9516.594.523.0213.202.634.814.355.375.74…NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN416.2016.964.502.9513.172.804.884.345.375.80…NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

5 rows × 3562 columns

JSON格式


sa = pd.read_json("Sarcasm_Headlines_Dataset.json", orient="records", lines=True)
sa.head()


sa.to_json("test.json", orient="records", lines=True)
pd.read_json("test.json", orient="records", lines=True)

article_linkheadlineis_sarcastic0https://www.huffingtonpost.com/entry/versace-b…former versace store clerk sues over secret ‘b…01https://www.huffingtonpost.com/entry/roseanne-…the ‘roseanne’ revival catches up to our thorn…02https://local.theonion.com/mom-starting-to-fea…mom starting to fear son’s web series closest …13https://politics.theonion.com/boehner-just-wan…boehner just wants wife to listen, not come up…14https://www.huffingtonpost.com/entry/jk-rowlin…j.k. rowling wishes snape happy birthday in th…0…………26704https://www.huffingtonpost.com/entry/american-…american politics in moral free-fall026705https://www.huffingtonpost.com/entry/americas-…america’s best 20 hikes026706https://www.huffingtonpost.com/entry/reparatio…reparations and obama026707https://www.huffingtonpost.com/entry/israeli-b…israeli ban targeting boycott supporters raise…026708https://www.huffingtonpost.com/entry/gourmet-g…gourmet gifts for the foodie 20140

26709 rows × 3 columns

Pandas高级处理

缺失值处理

movie = pd.read_csv("IMDB-Movie-Data.csv")
movie.head()

import numpy as np

np.any(pd.isnull(movie))

True

np.all(pd.notnull(movie))

False

pd.notnull(movie).all()

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


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


movie["Revenue (Millions)"].fillna(movie["Revenue (Millions)"].mean(), inplace=True)
movie["Metascore"].fillna(movie["Metascore"].mean(), inplace=True)
pd.notnull(movie).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

数据离散化

data = pd.read_csv("stock_day.csv")
p_change = data["p_change"]

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]2018-02-2700000001002018-02-2600000000102018-02-2300000001002018-02-2200000010002018-02-140000000100


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]2018-02-27000010002018-02-26000001002018-02-23000010002018-02-22000010002018-02-1400001000

合并


pd.concat([data, data2], axis=1).head()

openhighcloselowvolumeprice_changep_changema5ma10ma20…v_ma20turnoverrise_(-100, -7]rise_(-7, -5]rise_(-5, -3]rise_(-3, 0]rise_(0, 3]rise_(3, 5]rise_(5, 7]rise_(7, 100]2018-02-2723.5325.8824.1623.5395578.030.632.6822.94222.14222.875…55576.112.39000010002018-02-2622.8023.7823.5322.8060985.110.693.0222.40621.95522.942…56007.501.53000001002018-02-2322.8823.3722.8222.7152914.010.542.4221.93821.92923.022…56372.851.32000010002018-02-2222.2522.7622.2822.0236105.010.361.6421.44621.90923.137…60149.600.90000010002018-02-1421.4921.9921.9221.4823331.040.442.0521.36621.92323.253…61716.110.5800001000

5 rows × 22 columns

pd.concat([data, data2], axis=0).head()

openhighcloselowvolumeprice_changep_changema5ma10ma20…v_ma20turnoverrise_(-100, -7]rise_(-7, -5]rise_(-5, -3]rise_(-3, 0]rise_(0, 3]rise_(3, 5]rise_(5, 7]rise_(7, 100]2018-02-2723.5325.8824.1623.5395578.030.632.6822.94222.14222.875…55576.112.39NaNNaNNaNNaNNaNNaNNaNNaN2018-02-2622.8023.7823.5322.8060985.110.693.0222.40621.95522.942…56007.501.53NaNNaNNaNNaNNaNNaNNaNNaN2018-02-2322.8823.3722.8222.7152914.010.542.4221.93821.92923.022…56372.851.32NaNNaNNaNNaNNaNNaNNaNNaN2018-02-2222.2522.7622.2822.0236105.010.361.6421.44621.90923.137…60149.600.90NaNNaNNaNNaNNaNNaNNaNNaN2018-02-1421.4921.9921.9221.4823331.040.442.0521.36621.92323.253…61716.110.58NaNNaNNaNNaNNaNNaNNaNNaN

5 rows × 22 columns

pd.concat([data, data2], axis=0).tail()

openhighcloselowvolumeprice_changep_changema5ma10ma20…v_ma20turnoverrise_(-100, -7]rise_(-7, -5]rise_(-5, -3]rise_(-3, 0]rise_(0, 3]rise_(3, 5]rise_(5, 7]rise_(7, 100]2015-03-06NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN…NaNNaN0.00.00.00.00.00.00.01.02015-03-05NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN…NaNNaN0.00.00.00.01.00.00.00.02015-03-04NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN…NaNNaN0.00.00.00.01.00.00.00.02015-03-03NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN…NaNNaN0.00.00.00.01.00.00.00.02015-03-02NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN…NaNNaN0.00.00.00.01.00.00.00.0

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

key1key2AB0K0K0A0B01K0K1A1B12K1K0A2B23K2K1A3B3

right

key1key2CD0K0K0C0D01K1K0C1D12K1K0C2D23K2K0C3D3

pd.merge(left, right, how="inner", on=["key1", "key2"])

key1key2ABCD0K0K0A0B0C0D01K1K0A2B2C1D12K1K0A2B2C2D2

pd.merge(left, right, how="left", on=["key1", "key2"])

key1key2ABCD0K0K0A0B0C0D01K0K1A1B1NaNNaN2K1K0A2B2C1D13K1K0A2B2C2D24K2K1A3B3NaNNaN

pd.merge(left, right, how="right", on=["key1", "key2"])

key1key2ABCD0K0K0A0B0C0D01K1K0A2B2C1D12K1K0A2B2C2D23K2K0NaNNaNC3D3

pd.merge(left, right, how="outer", on=["key1", "key2"])

key1key2ABCD0K0K0A0B0C0D01K0K1A1B1NaNNaN2K1K0A2B2C1D13K1K0A2B2C2D24K2K1A3B3NaNNaN5K2K0NaNNaNC3D3


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)


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()

openhighcloselowvolumeprice_changep_changema5ma10ma20v_ma5v_ma10v_ma20turnoverweek2018-02-2723.5325.8824.1623.5395578.030.632.6822.94222.14222.87553782.6446738.6555576.112.3912018-02-2622.8023.7823.5322.8060985.110.693.0222.40621.95522.94240827.5242736.3456007.501.5302018-02-2322.8823.3722.8222.7152914.010.542.4221.93821.92923.02235119.5841871.9756372.851.3242018-02-2222.2522.7622.2822.0236105.010.361.6421.44621.90923.13735397.5839904.7860149.600.9032018-02-1421.4921.9921.9221.4823331.040.442.0521.36621.92323.25333590.2142935.7461716.110.582


data["pona"] = np.where(data["p_change"] > 0, 1, 0)
data.head()

openhighcloselowvolumeprice_changep_changema5ma10ma20v_ma5v_ma10v_ma20turnoverweekpona2018-02-2723.5325.8824.1623.5395578.030.632.6822.94222.14222.87553782.6446738.6555576.112.39112018-02-2622.8023.7823.5322.8060985.110.693.0222.40621.95522.94240827.5242736.3456007.501.53012018-02-2322.8823.3722.8222.7152914.010.542.4221.93821.92923.02235119.5841871.9756372.851.32412018-02-2222.2522.7622.2822.0236105.010.361.6421.44621.90923.13735397.5839904.7860149.600.90312018-02-1421.4921.9921.9221.4823331.040.442.0521.36621.92323.25333590.2142935.7461716.110.5821

交叉表和透视表


dataTable = pd.crosstab(data["week"], data["pona"])
dataTable

pona01week0636215576261713636545968

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'>
</axessubplot:xlabel='week'>

dataTable.div(dataTable.sum(axis=1), axis=0)

pona01week00.5040000.49600010.4198470.58015320.4621210.53787930.4921880.50781240.4645670.535433


data.pivot_table(["pona"], index=["week"])

ponaweek00.49600010.58015320.53787930.50781240.535433

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

colorobjectprice1price20whitepen5.564.751redpencil4.204.122greenpencil1.301.603redashtray0.560.754greenpen2.753.15


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？


movie = pd.read_csv("IMDB-Movie-Data.csv")
movie

RankTitleGenreDescriptionDirectorActorsYearRuntime (Minutes)RatingVotesRevenue (Millions)Metascore01Guardians of the GalaxyAction,Adventure,Sci-FiA group of intergalactic criminals are forced …James GunnChris Pratt, Vin Diesel, Bradley Cooper, Zoe S…20141218.1757074333.1376.012PrometheusAdventure,Mystery,Sci-FiFollowing clues to the origin of mankind, a te…Ridley ScottNoomi Rapace, Logan Marshall-Green, Michael Fa…20121247.0485820126.4665.023SplitHorror,ThrillerThree girls are kidnapped by a man with a diag…M. Night ShyamalanJames McAvoy, Anya Taylor-Joy, Haley Lu Richar…20161177.3157606138.1262.034SingAnimation,Comedy,FamilyIn a city of humanoid animals, a hustling thea…Christophe LourdeletMatthew McConaughey,Reese Witherspoon, Seth Ma…20161087.260545270.3259.045Suicide SquadAction,Adventure,FantasyA secret government agency recruits some of th…David AyerWill Smith, Jared Leto, Margot Robbie, Viola D…20161236.2393727325.0240.0…………………………………995996Secret in Their EyesCrime,Drama,MysteryA tight-knit team of rising investigators, alo…Billy RayChiwetel Ejiofor, Nicole Kidman, Julia Roberts…20151116.227585NaN45.0996997Hostel: Part IIHorrorThree American college students studying abroa…Eli RothLauren German, Heather Matarazzo, Bijou Philli…2007945.57315217.5446.0997998Step Up 2: The StreetsDrama,Music,RomanceRomantic sparks occur between two dance studen…Jon M. ChuRobert Hoffman, Briana Evigan, Cassie Ventura,…2008986.27069958.0150.0998999Search PartyAdventure,ComedyA pair of friends embark on a mission to reuni…Scot ArmstrongAdam Pally, T.J. Miller, Thomas Middleditch,Sh…2014935.64881NaN22.09991000Nine LivesComedy,Family,FantasyA stuffy businessman finds himself trapped ins…Barry SonnenfeldKevin Spacey, Jennifer Garner, Robbie Amell,Ch…2016875.31243519.6411.0

1000 rows × 12 columns


movie["Rating"].mean()

6.723199999999999


np.unique(movie["Director"]).size


movie["Rating"].plot(kind="hist", figsize=(20, 8))

<axessubplot:ylabel='frequency'>
</axessubplot:ylabel='frequency'>

import matplotlib.pyplot as plt

plt.figure(figsize=(20, 8), dpi=80)

plt.hist(movie["Rating"], 20)

plt.xticks(np.linspace(movie["Rating"].min(), movie["Rating"].max(), 21))

plt.grid(linestyle="--", alpha=0.5)

plt.show()


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'],
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 ['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') < code></u9')>


count = pd.DataFrame(np.zeros(shape=[1000,20],dtype="int32"),columns=genres)
count.head()

ActionAdventureAnimationBiographyComedyCrimeDramaFamilyFantasyHistoryHorrorMusicMusicalMysteryRomanceSci-FiSportThrillerWarWestern000000000000000000000100000000000000000000200000000000000000000300000000000000000000400000000000000000000


for i in range(1000):
    count.loc[i, movie_genre[i]] = 1
count

ActionAdventureAnimationBiographyComedyCrimeDramaFamilyFantasyHistoryHorrorMusicMusicalMysteryRomanceSci-FiSportThrillerWarWestern011000000000000010000101000000000001010000200000000001000000100300101001000000000000411000000100000000000………………………………………………………9950000011000000100000099600000000001000000000997000000100001001000009980100100000000000000099900001001100000000000

1000 rows × 20 columns

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

<axessubplot:ylabel='none'>
</axessubplot:ylabel='none'>

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

<axessubplot:>
</axessubplot:>

Original: https://blog.csdn.net/qq_46039856/article/details/122771239
Author: Gettler•Main
Title: Pandas 学习笔记二

原创文章受到原创版权保护。转载请注明出处：https://www.johngo689.com/699298/

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Pandas 学习笔记二

文章目录

Pandas 学习笔记二

数据的读取与存储

csv格式

hdf5格式

JSON格式

Pandas高级处理

缺失值处理

数据离散化

合并

交叉表和透视表

分组与聚合

实战案例 电影数据分析练习

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实战案例电影数据分析练习