数据分析工具pandas快速入门教程1-开胃菜

简介

Pandas是用于数据分析的开源Python库,也是目前数据分析最重要的开源库。它能够处理类似电子表格的数据，用于快速数据加载，操作，对齐，合并等。为Python提供这些增强功能，Pandas的数据类型为：Series和DataFrame。DataFrame为整个电子表格或矩形数据，而Series是DataFrame的列。DataFrame也可以被认为是字典或Series的集合。

加载数据

load.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# load.py

import pandas as pd

df = pd.read_csv(r"../data/gapminder.tsv", sep='\t') 

print("\n\n查看前五行")
print(df.head())

print("\n\n查看类型")
print(type(df))

print("\n\n查看大小")
print(df.shape)

print("\n\n查看列名")
print(df.columns)

print("\n\n查看dtypes(基于列)")
print(df.dtypes)

print("\n\n查看统计信息")
print(df.info())

执行结果

$ ./load.py 


查看前五行
       country continent  year  lifeExp       pop   gdpPercap
0  Afghanistan      Asia  1952   28.801   8425333  779.445314
1  Afghanistan      Asia  1957   30.332   9240934  820.853030
2  Afghanistan      Asia  1962   31.997  10267083  853.100710
3  Afghanistan      Asia  1967   34.020  11537966  836.197138
4  Afghanistan      Asia  1972   36.088  13079460  739.981106


查看类型
<class 'pandas.core.frame.DataFrame'>


查看大小
(1704, 6)


查看列名
Index(['country', 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap'], dtype='object')


查看dtypes(基于列)
country       object
continent     object
year           int64
lifeExp      float64
pop            int64
gdpPercap    float64
dtype: object


查看统计信息
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1704 entries, 0 to 1703
Data columns (total 6 columns):
country      1704 non-null object
continent    1704 non-null object
year         1704 non-null int64
lifeExp      1704 non-null float64
pop          1704 non-null int64
gdpPercap    1704 non-null float64
dtypes: float64(2), int64(2), object(2)
memory usage: 80.0+ KB
None

Pandas类型	Python类型
object	string
int64	int
float64	float
datetime64	datetime

行列与单元格

col.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# col.py

import pandas as pd

df = pd.read_csv(r"../data/gapminder.tsv", sep='\t') 

# 列操作
country_df = df['country'] # 列名选取单列

print("\n\n列首5行")
print(country_df.head())

print("\n\n列尾5行")
print(country_df.tail())

country_df_dot = df.country # 点号的方式选取列
print("\n\n点号的方式选取列")
print(country_df_dot.head())

subset = df[['country', 'continent', 'year']] # 选取多列
print("\n\n选取多列")
print(subset.head())

执行结果

$ ./col.py 


列首5行
0    Afghanistan
1    Afghanistan
2    Afghanistan
3    Afghanistan
4    Afghanistan
Name: country, dtype: object


列尾5行
1699    Zimbabwe
1700    Zimbabwe
1701    Zimbabwe
1702    Zimbabwe
1703    Zimbabwe
Name: country, dtype: object


点号的方式选取列
0    Afghanistan
1    Afghanistan
2    Afghanistan
3    Afghanistan
4    Afghanistan
Name: country, dtype: object


选取多列
       country continent  year
0  Afghanistan      Asia  1952
1  Afghanistan      Asia  1957
2  Afghanistan      Asia  1962
3  Afghanistan      Asia  1967
4  Afghanistan      Asia  1972

row.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# row.py

import pandas as pd

df = pd.read_csv(r"../data/gapminder.tsv", sep='\t') 

# 行操作，注意df.loc[-1]是非法的
print("\n\n第一行")
print(df.loc[0])

print("\n\n行数")
number_of_rows = df.shape[0]
print(number_of_rows)

last_row_index = number_of_rows - 1
print("\n\n最后一行")
print(df.loc[last_row_index])

print("\n\ntail的方法输出最后一行")
print(df.tail(n=1))

subset_loc = df.loc[0]
subset_head = df.head(n=1)
print("\n\nloc的类型为序列Series")
print(type(subset_loc))

print("\n\nhead的类型为数据帧DataFrame")
print(type(subset_head))

print("\n\nloc选取三列，类型为数据帧DataFrame")
print(df.loc[[0, 99, 999]])
print(type(df.loc[[0, 99, 999]]))

print("\n\niloc选取第一行")
print(df.iloc[0])

print("\n\niloc选取三行")
print(df.iloc[[0, 99, 999]])

执行结果

$ ./row.py 


第一行
country      Afghanistan
continent           Asia
year                1952
lifeExp           28.801
pop              8425333
gdpPercap        779.445
Name: 0, dtype: object


行数
1704


最后一行
country      Zimbabwe
continent      Africa
year             2007
lifeExp        43.487
pop          12311143
gdpPercap     469.709
Name: 1703, dtype: object


tail的方法输出最后一行
       country continent  year  lifeExp       pop   gdpPercap
1703  Zimbabwe    Africa  2007   43.487  12311143  469.709298


loc的类型为序列Series
<class 'pandas.core.series.Series'>


head的类型为数据帧DataFrame
<class 'pandas.core.frame.DataFrame'>


loc选取三列，类型为数据帧DataFrame
         country continent  year  lifeExp       pop    gdpPercap
0    Afghanistan      Asia  1952   28.801   8425333   779.445314
99    Bangladesh      Asia  1967   43.453  62821884   721.186086
999     Mongolia      Asia  1967   51.253   1149500  1226.041130
<class 'pandas.core.frame.DataFrame'>


iloc选取第一行
country      Afghanistan
continent           Asia
year                1952
lifeExp           28.801
pop              8425333
gdpPercap        779.445
Name: 0, dtype: object


iloc选取三行
         country continent  year  lifeExp       pop    gdpPercap
0    Afghanistan      Asia  1952   28.801   8425333   779.445314
99    Bangladesh      Asia  1967   43.453  62821884   721.186086
999     Mongolia      Asia  1967   51.253   1149500  1226.041130

mix.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Author:    xurongzhong#126.com wechat:pythontesting qq:37391319
# qq群：144081101 591302926  567351477
# CreateDate: 2018-06-07
# mix.py

import pandas as pd

df = pd.read_csv(r"../data/gapminder.tsv", sep='\t') 

# 混合选取
print("\n\nloc选取坐标")
print(df.loc[42, 'country'])

print("\n\niloc选取坐标")
print(df.iloc[42, 0])

print("\n\nloc选取子集")
print(df.loc[[0, 99, 999], ['country', 'lifeExp', 'gdpPercap']])

执行结果

#!python

$ ./mix.py 


loc选取坐标
Angola


iloc选取坐标
Angola


loc选取子集
         country  lifeExp    gdpPercap
0    Afghanistan   28.801   779.445314
99    Bangladesh   43.453   721.186086
999     Mongolia   51.253  1226.041130

分组和聚合

group.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# group.py

import pandas as pd

df = pd.read_csv(r"../data/gapminder.tsv", sep='\t') 

print("\n\n年人均产值")
print(df.groupby('year')['lifeExp'].mean())

print("\n\n基于年分组")
grouped_year_df = df.groupby('year')
print(type(grouped_year_df))
print(grouped_year_df)

print("\n\nlifeExp")
grouped_year_df_lifeExp = grouped_year_df['lifeExp']
print(type(grouped_year_df_lifeExp))
print(grouped_year_df_lifeExp)

print("\n\n年平均产值")
mean_lifeExp_by_year = grouped_year_df_lifeExp.mean()
print(mean_lifeExp_by_year)

print("\n\n基于年和洲分组")
print(df.groupby(['year', 'continent'])[['lifeExp',
'gdpPercap']].mean())

print("\n\n统计每个洲的国家数")
print(df.groupby('continent')['country'].nunique())

执行结果

#!python

$ ./group.py 


年人均产值
year
1952    49.057620
1957    51.507401
1962    53.609249
1967    55.678290
1972    57.647386
1977    59.570157
1982    61.533197
1987    63.212613
1992    64.160338
1997    65.014676
2002    65.694923
2007    67.007423
Name: lifeExp, dtype: float64


基于年分组
<class 'pandas.core.groupby.groupby.DataFrameGroupBy'>
<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x7f0e2b0c89e8>


lifeExp
<class 'pandas.core.groupby.groupby.SeriesGroupBy'>
<pandas.core.groupby.groupby.SeriesGroupBy object at 0x7f0e151e2f28>


年平均产值
year
1952    49.057620
1957    51.507401
1962    53.609249
1967    55.678290
1972    57.647386
1977    59.570157
1982    61.533197
1987    63.212613
1992    64.160338
1997    65.014676
2002    65.694923
2007    67.007423
Name: lifeExp, dtype: float64


基于年和洲分组
                  lifeExp     gdpPercap
year continent                         
1952 Africa     39.135500   1252.572466
     Americas   53.279840   4079.062552
     Asia       46.314394   5195.484004
     Europe     64.408500   5661.057435
     Oceania    69.255000  10298.085650
1957 Africa     41.266346   1385.236062
     Americas   55.960280   4616.043733
     Asia       49.318544   5787.732940
     Europe     66.703067   6963.012816
     Oceania    70.295000  11598.522455
1962 Africa     43.319442   1598.078825
     Americas   58.398760   4901.541870
     Asia       51.563223   5729.369625
     Europe     68.539233   8365.486814
     Oceania    71.085000  12696.452430
1967 Africa     45.334538   2050.363801
     Americas   60.410920   5668.253496
     Asia       54.663640   5971.173374
     Europe     69.737600  10143.823757
     Oceania    71.310000  14495.021790
1972 Africa     47.450942   2339.615674
     Americas   62.394920   6491.334139
     Asia       57.319269   8187.468699
     Europe     70.775033  12479.575246
     Oceania    71.910000  16417.333380
1977 Africa     49.580423   2585.938508
     Americas   64.391560   7352.007126
     Asia       59.610556   7791.314020
     Europe     71.937767  14283.979110
     Oceania    72.855000  17283.957605
1982 Africa     51.592865   2481.592960
     Americas   66.228840   7506.737088
     Asia       62.617939   7434.135157
     Europe     72.806400  15617.896551
     Oceania    74.290000  18554.709840
1987 Africa     53.344788   2282.668991
     Americas   68.090720   7793.400261
     Asia       64.851182   7608.226508
     Europe     73.642167  17214.310727
     Oceania    75.320000  20448.040160
1992 Africa     53.629577   2281.810333
     Americas   69.568360   8044.934406
     Asia       66.537212   8639.690248
     Europe     74.440100  17061.568084
     Oceania    76.945000  20894.045885
1997 Africa     53.598269   2378.759555
     Americas   71.150480   8889.300863
     Asia       68.020515   9834.093295
     Europe     75.505167  19076.781802
     Oceania    78.190000  24024.175170
2002 Africa     53.325231   2599.385159
     Americas   72.422040   9287.677107
     Asia       69.233879  10174.090397
     Europe     76.700600  21711.732422
     Oceania    79.740000  26938.778040
2007 Africa     54.806038   3089.032605
     Americas   73.608120  11003.031625
     Asia       70.728485  12473.026870
     Europe     77.648600  25054.481636
     Oceania    80.719500  29810.188275


统计每个洲的国家数
continent
Africa      52
Americas    25
Asia        33
Europe      30
Oceania      2
Name: country, dtype: int64

基本绘图

import pandas as pd

df = pd.read_csv(r"../data/gapminder.tsv", sep='\t') 

global_yearly_life_expectancy = df.groupby('year')['lifeExp'].mean()
print(global_yearly_life_expectancy)

global_yearly_life_expectancy.plot()

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