Python Data Analysis Library 或 pandas 是基于NumPy 的一种工具，该工具是为了解决数据分析任务而创建的。Pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的工具。pandas提供了大量能使我们快速便捷地处理数据的函数和方法。你很快就会发现，它是使Python成为强大而高效的数据分析环境的重要因素之一。
本文介绍了Pandas的基础用法

Github: https://github.com/yingzk/pandas_learning
本文PDF下载:https://www.yingjoy.cn/downloads/pandas_pdf.zip

库的导入

# 导入numpy库并重命名为np
import numpy as np
# 导入pandas 库并重命名为pd
import pandas as pd

数据的导入

pd.read_csv(filename)   # 从csv导入
pd.read_table(filename) # 导入有分隔符的文本 (如TSV) 中的数据
pd.read_excel(filename) # 从excel导入
pd.read_sql(query, connection_object) # 导入SQL数据表/数据库中的数据
pd.read_json(json_string) # 导入JSON格式的字符，URL地址或者文件中的数据
pd.read_html(url) # 导入经过解析的URL地址中包含的数据框 (DataFrame) 数据
pd.read_clipboard() # 导入系统粘贴板里面的数据
pd.DataFrame(dict)  # 导入Python字典 (dict) 里面的数据，其中key是数据框的表头，value是数据框的内容。

数据的导出

df.to_csv(filename) # 将数据框 (DataFrame)中的数据导入csv格式的文件中
df.to_excel(filename) # 将数据框 (DataFrame)中的数据导入Excel格式的文件中
df.to_sql(table_name,connection_object) # 将数据框 (DataFrame)中的数据导入SQL数据表/数据库中
df.to_json(filename) # 将数据框 (DataFrame)中的数据导入JSON格式的文件中

创建测试对象

pd.DataFrame(np.random.rand(5, 10)) # 创建一个5列10行的由随机浮点数组成的数据框 DataFrame

	0	1	2	3	4	5	6	7	8	9
0	0.016860	0.855994	0.992872	0.652278	0.517510	0.742986	0.452981	0.568701	0.795436	0.622609
1	0.476801	0.190823	0.450436	0.912401	0.335651	0.197766	0.042523	0.580323	0.498982	0.473128
2	0.029820	0.886500	0.902864	0.465084	0.380933	0.033583	0.928827	0.501687	0.857512	0.671840
3	0.897254	0.413717	0.991061	0.393033	0.388630	0.661025	0.635417	0.695609	0.305378	0.147508
4	0.573882	0.786888	0.177782	0.864474	0.594416	0.765678	0.217279	0.446570	0.930604	0.686823

pd.Series(my_list) # 从一个可迭代的对象 my_list 中创建一个数据组

my_list = ['abc',123,'HelloWorld', 5.7]
pd.Series(my_list)

0           abc
1           123
2    HelloWorld
3           5.7
dtype: object

df = pd.DataFrame(np.random.rand(10, 5))
df.index = pd.date_range('2017/1/1', periods=df.shape[0])
df

	0	1	2	3	4
2017-01-01	0.011762	0.634116	0.045220	0.452117	0.879969
2017-01-02	0.802262	0.661908	0.214822	0.444259	0.200370
2017-01-03	0.301050	0.004534	0.881042	0.825632	0.331118
2017-01-04	0.095324	0.916430	0.177795	0.191502	0.546973
2017-01-05	0.482868	0.953719	0.615461	0.868984	0.639286
2017-01-06	0.958404	0.155357	0.293012	0.115218	0.177846
2017-01-07	0.915488	0.486922	0.440474	0.584764	0.271243
2017-01-08	0.480413	0.600622	0.325212	0.532259	0.687718
2017-01-09	0.859887	0.236677	0.635073	0.811840	0.497289
2017-01-10	0.024623	0.635122	0.346393	0.860260	0.325502

数据的查看

df.head(n)  # 查看前n行的数据
df.tail(n)  # 查看后n行的数据

df = pd.DataFrame(np.random.rand(10, 5))
df.head(3)

	0	1	2	3	4
0	0.996381	0.440502	0.583701	0.120444	0.241775
1	0.126877	0.646841	0.740163	0.764182	0.810129
2	0.254386	0.451341	0.288513	0.515995	0.146529

df = pd.DataFrame(np.random.rand(10, 5))
df.tail(3)

	0	1	2	3	4
7	0.466316	0.747013	0.568442	0.562552	0.949529
8	0.243633	0.605133	0.114011	0.898604	0.024648
9	0.155605	0.799580	0.160883	0.986743	0.446114

查看数据的形状

df.shape # 查看数据的形状（行和宽）

查看数据的相关信息

df.info() # 查看数据的索引、数据类型及内存信息

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 5 columns):
0    10 non-null float64
1    10 non-null float64
2    10 non-null float64
3    10 non-null float64
4    10 non-null float64
dtypes: float64(5)
memory usage: 480.0 bytes

df.describe() # 对于数据类型为数值型的列，查询其描述性统计的内容

	0	1	2	3	4
count	10.000000	10.000000	10.000000	10.000000	10.000000
mean	0.432296	0.601318	0.444123	0.543053	0.413944
std	0.268632	0.216483	0.298076	0.284759	0.345554
min	0.155605	0.252712	0.114011	0.126172	0.024648
25%	0.245740	0.461876	0.191090	0.295224	0.094655
50%	0.353927	0.609304	0.401004	0.565072	0.389053
75%	0.559964	0.762463	0.556721	0.693000	0.668621
max	0.985457	0.932679	0.990827	0.986743	0.949529

统计次数

s.value_counts(dropna=False) # 查询每个独特数据值出现次数统计

s = pd.Series([1,2,3,3,4,np.nan,5,5,5,6,7])
s.value_counts(dropna=False)

 5.0    3
 3.0    2
 7.0    1
 6.0    1
NaN     1
 4.0    1
 2.0    1
 1.0    1
dtype: int64

s = s.apply(lambda x: x+1)
s

0     3.0
1     4.0
2     5.0
3     5.0
4     6.0
5     NaN
6     7.0
7     7.0
8     7.0
9     8.0
10    9.0
dtype: float64

df.apply(pd.Series.value_counts) # 查询数据框 (Data Frame) 中每个列的独特数据值出现次数统计

数据选取

df[col] # 以数组 Series 的形式返回选取的列

df = pd.DataFrame(np.random.rand(5, 5), columns=list('ABCDE'))
df['C']

0    0.452717
1    0.407755
2    0.549391
3    0.759433
4    0.153871
Name: C, dtype: float64

df[[col1, col2]] # 选择多列

df = pd.DataFrame(np.random.rand(5, 5), columns=list('ABCDE'))
df[['C', 'D']]

	C	D
0	0.431885	0.304796
1	0.028960	0.187738
2	0.176520	0.102980
3	0.370277	0.098031
4	0.247122	0.345735

s.iloc[0] # 按位置选取

s = pd.Series(np.array(['I', 'Love', 'China']))
s.iloc[0]

'I'

s.loc['index_one'] # 按索引选取

s = pd.Series(np.array(['I', 'Love', 'China']))
s.loc[0]

'I'

df.DataFrame[n, :] #选取第n行

df = pd.DataFrame(np.array([['I', 'Love', 'China'], ['I', 'Love', 'Data']]))
df.iloc[1, :]

0       I
1    Love
2    Data
Name: 1, dtype: object

df.iloc[0, 0] # 选取第一个元素

df = pd.DataFrame(np.random.rand(5, 5))
df

	0	1	2	3	4
0	0.786709	0.405902	0.151383	0.384778	0.871664
1	0.491006	0.774710	0.388011	0.758102	0.762115
2	0.085647	0.543243	0.582565	0.664243	0.379896
3	0.806211	0.794284	0.968755	0.883923	0.354820
4	0.463902	0.481756	0.131181	0.590878	0.801769

df.iloc[0, 0]

0.78670886755075187

数据的清洗

df.columns = ['a', 'b'] # 对列名重新命名

df = pd.DataFrame({'A':np.array([1,np.nan,2,3,6,np.nan]),
                   'B':np.array([np.nan,4,np.nan,5,9,np.nan]),
                   'C':'foo'})
df

	A	B	C
0	1.0	NaN	foo
1	NaN	4.0	foo
2	2.0	NaN	foo
3	3.0	5.0	foo
4	6.0	9.0	foo
5	NaN	NaN	foo

df.columns = ['a', 'b', 'c']
df

	a	b	c
0	1.0	NaN	foo
1	NaN	4.0	foo
2	2.0	NaN	foo
3	3.0	5.0	foo
4	6.0	9.0	foo
5	NaN	NaN	foo

pd.isnull() # 检查数据中出现空值的情况, 返回一个布尔型的列
pd.notnull() #相对应isnull 返回不是空值的情况

df = pd.DataFrame({'A':np.array([1,np.nan,2,3,6,np.nan]),
                   'B':np.array([np.nan,4,np.nan,5,9,np.nan]),
                   'C':'foo'})
df.isnull()

	A	B	C
0	False	True	False
1	True	False	False
2	False	True	False
3	False	False	False
4	False	False	False
5	True	True	False

df.isnull().sum() # 对每一列的空值进行统计

A    2
B    3
C    0
dtype: int64

df.dropna(axis = 0, thresh=n) # 删除包含缺失值的行  axis = 1时删除列  # thresh = n移除空值超过(包括等于)n的行

df

	A	B	C
0	1.0	NaN	foo
1	NaN	4.0	foo
2	2.0	NaN	foo
3	3.0	5.0	foo
4	6.0	9.0	foo
5	NaN	NaN	foo

df.dropna(axis = 0)

	A	B	C
3	3.0	5.0	foo
4	6.0	9.0	foo

df.dropna(axis = 1)

	C
0	foo
1	foo
2	foo
3	foo
4	foo
5	foo

df.dropna(axis = 0, thresh = 2)

	A	B	C
0	1.0	NaN	foo
1	NaN	4.0	foo
2	2.0	NaN	foo
3	3.0	5.0	foo
4	6.0	9.0	foo

df.fillna(df.mean()) # 用平均值来填充空值

s = pd.Series([1,3,5,np.nan,7,9,9])
s.fillna(s.mean())

0    1.000000
1    3.000000
2    5.000000
3    5.666667
4    7.000000
5    9.000000
6    9.000000
dtype: float64

s.astype(type) # 转换列的类型

s = pd.Series([1,3,5,np.nan,7,9,9])
s.fillna(s.mean()).astype(int)

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

s.replace(1, 'one') # 将Series中的1替换为one

s = pd.Series([1,3,5,np.nan,7,9,9])
s.replace(1,'one')

0    one
1      3
2      5
3    NaN
4      7
5      9
6      9
dtype: object

s.replace([1,3],['one','three']) # 将数组(Series)中所有的1替换为'one', 所有的3替换为'three'

s = pd.Series([1,3,5,np.nan,7,9,9])
s.replace([1,3],['one','three'])

0      one
1    three
2        5
3      NaN
4        7
5        9
6        9
dtype: object

df.rename(columns=lambda x: x + 2) # 将全体列重命名

df = pd.DataFrame(np.random.rand(4,4))
df

	0	1	2	3
0	0.669102	0.548996	0.512802	0.449220
1	0.108840	0.974720	0.665050	0.271009
2	0.146804	0.060744	0.637770	0.383380
3	0.108163	0.893999	0.216907	0.730504

df.rename(columns=lambda x: x+ 2)

	2	3	4	5
0	0.669102	0.548996	0.512802	0.449220
1	0.108840	0.974720	0.665050	0.271009
2	0.146804	0.060744	0.637770	0.383380
3	0.108163	0.893999	0.216907	0.730504

df.rename(columns={'old_name': 'new_ name'}) # 将选择的列重命名

df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))
df.rename(columns={'A':'New A', 'B':'New B'})

	New A	New B	C	D	E
0	0.680941	0.766561	0.486226	0.301537	0.289970
1	0.917036	0.100054	0.464342	0.181454	0.933591
2	0.253549	0.766181	0.085607	0.969627	0.630674
3	0.377840	0.909920	0.214338	0.011844	0.392257
4	0.608564	0.587614	0.039867	0.630492	0.402101
5	0.361074	0.937618	0.787055	0.054157	0.300325
6	0.605472	0.608429	0.052152	0.669343	0.745648
7	0.660738	0.158713	0.352756	0.028325	0.195899
8	0.855695	0.578177	0.447043	0.093923	0.316234
9	0.337392	0.645260	0.140221	0.616652	0.727144

df.set_index('column_one') # 改变索引

df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))
df.set_index('B')

	A	C	D	E
B
0.824403	0.013092	0.055626	0.895268	0.837350
0.310559	0.689064	0.541375	0.275461	0.808554
0.533495	0.072835	0.563758	0.695029	0.524957
0.541211	0.820817	0.591130	0.268978	0.546996
0.286587	0.936692	0.343227	0.383610	0.811302
0.878391	0.938883	0.636148	0.776493	0.025840
0.156482	0.918591	0.030869	0.235020	0.096212
0.857049	0.613991	0.810541	0.917927	0.921329
0.713271	0.949683	0.811386	0.920452	0.213173
0.686945	0.522276	0.881299	0.936260	0.030993

df.rename(index = lambda x: x+ 1) # 改变全体索引

df = pd.DataFrame(np.random.rand(10,5))
df.rename(index = lambda x: x+ 1)

	0	1	2	3	4
1	0.382337	0.185501	0.457958	0.009713	0.628963
2	0.024175	0.223274	0.698171	0.071715	0.063272
3	0.913995	0.713092	0.269621	0.575365	0.805266
4	0.612708	0.220953	0.090858	0.425472	0.018996
5	0.045363	0.153343	0.730828	0.323554	0.364821
6	0.462096	0.614072	0.993130	0.988894	0.788648
7	0.887381	0.802119	0.191248	0.980064	0.628450
8	0.138270	0.922870	0.250827	0.297472	0.289915
9	0.258687	0.807993	0.930009	0.811335	0.609763
10	0.588020	0.392127	0.590799	0.923180	0.722801

数据的过滤(filter),排序(sort)和分组(groupby)

df[df[col] > 0.5] # 选取数据df中对应行的数值大于0.5的全部列  支持逻辑运算

df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))
df

	A	B	C	D	E
0	0.051900	0.548808	0.744936	0.848002	0.299505
1	0.979053	0.216078	0.394286	0.520654	0.584194
2	0.185679	0.453151	0.839947	0.730177	0.392377
3	0.161267	0.981833	0.890858	0.613972	0.467528
4	0.091140	0.369805	0.600035	0.372857	0.897063
5	0.612195	0.981150	0.578304	0.220064	0.488182
6	0.898736	0.626289	0.788306	0.747086	0.386097
7	0.568531	0.362593	0.644950	0.510410	0.092556
8	0.872898	0.771917	0.853365	0.227531	0.045184
9	0.898296	0.683850	0.138142	0.956854	0.335476

df[df['A'] > 0.5]

	A	B	C	D	E
1	0.979053	0.216078	0.394286	0.520654	0.584194
5	0.612195	0.981150	0.578304	0.220064	0.488182
6	0.898736	0.626289	0.788306	0.747086	0.386097
7	0.568531	0.362593	0.644950	0.510410	0.092556
8	0.872898	0.771917	0.853365	0.227531	0.045184
9	0.898296	0.683850	0.138142	0.956854	0.335476

df[(df['A'] > 0.5) & (df['B'] < 0.7)]

	A	B	C	D	E
1	0.979053	0.216078	0.394286	0.520654	0.584194
6	0.898736	0.626289	0.788306	0.747086	0.386097
7	0.568531	0.362593	0.644950	0.510410	0.092556
9	0.898296	0.683850	0.138142	0.956854	0.335476

排序

df.sort_values(col, ascending=True) #按照列进行排序  # ascending: True 升序 False 降序

df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))
df.sort_values('A', ascending=True)

	A	B	C	D	E
3	0.015834	0.758417	0.123415	0.802403	0.782450
1	0.068046	0.373240	0.414358	0.105285	0.759001
7	0.134238	0.104416	0.551595	0.472277	0.015997
4	0.236628	0.391852	0.390275	0.904988	0.650108
8	0.469382	0.426359	0.137109	0.253183	0.894667
2	0.508937	0.443894	0.147076	0.149885	0.434802
0	0.572640	0.369032	0.412343	0.402019	0.445365
9	0.663964	0.533604	0.217605	0.602667	0.637232
6	0.765109	0.646277	0.885381	0.743307	0.649711
5	0.962494	0.650830	0.754514	0.578115	0.659846

df.sort_values([col1,col2],ascending=[True,False]) # 按照数据框的列col1升序，col2降序的方式对数据框df做排序

df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))
df.sort_values(['A','E'],ascending=[True,False])

	A	B	C	D	E
1	0.083526	0.969267	0.012550	0.672851	0.866501
7	0.107839	0.383900	0.982337	0.390914	0.308559
0	0.148742	0.306888	0.853949	0.144650	0.414474
4	0.190011	0.794060	0.514756	0.272207	0.086894
6	0.423982	0.229873	0.992318	0.495706	0.971735
9	0.532263	0.106900	0.528114	0.456583	0.362642
2	0.619678	0.800373	0.927766	0.742667	0.645809
3	0.815312	0.920682	0.833351	0.266840	0.132698
5	0.842293	0.049499	0.780198	0.343752	0.341800
8	0.932379	0.398721	0.080358	0.200681	0.549237

分组

df.groupby(col) # 按照某列对数据框df做分组 # 常与count进行连用，统计出各词的个数

df = pd.DataFrame({'A':np.array(['foo','foo','foo','foo','bar','bar']),
      'B':np.array(['one','one','two','two','three','three']),
     'C':np.array(['small','medium','large','large','small','small']),
     'D':np.array([1,2,2,3,3,5])})
df

	A	B	C	D
0	foo	one	small	1
1	foo	one	medium	2
2	foo	two	large	2
3	foo	two	large	3
4	bar	three	small	3
5	bar	three	small	5

df.groupby('B').count()

	A	C	D
B
one	2	2	2
three	2	2	2
two	2	2	2

df.groupby([col1,col2]) # 按照列col1和col2对数据框df做分组

df = pd.DataFrame({'A':np.array(['foo','foo','foo','foo','bar','bar']),
      'B':np.array(['one','one','two','two','three','three']),
     'C':np.array(['small','medium','large','large','small','small']),
     'D':np.array([1,2,2,3,3,5])})
df.groupby(['A', 'B']).sum()

		D
A	B
bar	three	8
foo	one	3
two	5

df.groupby(col1)[col2].mean() # 按照列col1对数据框df做分组处理后，返回对应的col2的平均值

df = pd.DataFrame({'A':np.array(['foo','foo','foo','foo','bar','bar']),
      'B':np.array(['one','one','two','two','three','three']),
     'C':np.array(['small','medium','large','large','small','small']),
     'D':np.array([1,2,2,3,3,5])})
df.groupby('A')['D'].mean()

A
bar    4
foo    2
Name: D, dtype: int32

df.pivot_table(index=col1,values=[col2,col3],aggfunc=mean) # 做透视表，索引为col1,针对的数值列为col2和col3，分组函数为平均值

df = pd.DataFrame({'A':np.array(['foo','foo','foo','foo','bar','bar']),
      'B':np.array(['one','one','two','two','three','three']),
     'C':np.array(['small','medium','large','large','small','small']),
     'D':np.array([1,2,2,3,3,5])})
df.pivot_table(df, index=['A', 'B'], columns=['C'], aggfunc=np.sum)

		D
	C	large	medium	small
A	B
bar	three	NaN	NaN	8.0
foo	one	NaN	2.0	1.0
two	5.0	NaN	NaN

df = pd.DataFrame({'A':np.array(['foo','foo','foo','foo','bar','bar']),
      'B':np.array(['one','one','two','two','three','three']),
     'C':np.array(['small','medium','large','large','small','small']),
     'D':np.array([1,2,2,3,3,5])})
df.groupby('A').agg(np.mean)

	D
A
bar	4
foo	2

df.apply(np.mean, axis=0) # 对数据框df的每一列求平均值 axis: 0对列名（横着的）进行处理  1对索引（竖着的）进行处理

df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))
df

	A	B	C	D	E
0	0.800902	0.933676	0.461338	0.353398	0.057885
1	0.988580	0.681318	0.533361	0.486016	0.220004
2	0.695034	0.643920	0.694040	0.280063	0.641867
3	0.925290	0.084906	0.120247	0.880991	0.399596
4	0.697742	0.372860	0.881456	0.565627	0.272549
5	0.614245	0.658123	0.797487	0.609511	0.544633
6	0.153517	0.354870	0.910838	0.416895	0.098821
7	0.088223	0.501401	0.702754	0.334938	0.182708
8	0.737348	0.569340	0.291342	0.847058	0.193331
9	0.083915	0.396210	0.589415	0.806525	0.598841

df.apply(np.mean, axis=0)

A    0.578480
B    0.519662
C    0.598228
D    0.558102
E    0.321024
dtype: float64

数据的连接(join)与组合(combine)

df1.append(df2) # 在数据框df2的末尾添加数据框df1，其中df1和df2的列数应该相等  列合并

df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3'],
                    'C': ['C0', 'C1', 'C2', 'C3'],
                    'D': ['D0', 'D1', 'D2', 'D3']},
                   index=[0, 1, 2, 3])
df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
                    'B': ['B4', 'B5', 'B6', 'B7'],
                    'C': ['C4', 'C5', 'C6', 'C7'],
                    'D': ['D4', 'D5', 'D6', 'D7']},
                   index=[4, 5, 6, 7])
df1.append(df2)

	A	B	C	D
0	A0	B0	C0	D0
1	A1	B1	C1	D1
2	A2	B2	C2	D2
3	A3	B3	C3	D3
4	A4	B4	C4	D4
5	A5	B5	C5	D5
6	A6	B6	C6	D6
7	A7	B7	C7	D7

pd.concat([df1, df2], axis=1) # 在数据框df1的列最后添加数据框df2,其中df1和df2的行数应该相等  # 中括号可以换成圆括号  # axis: 0进行行合并  1进行列合并

df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3'],
                    'C': ['C0', 'C1', 'C2', 'C3'],
                    'D': ['D0', 'D1', 'D2', 'D3']},
                   index=[0, 1, 2, 3])
df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
                    'B': ['B4', 'B5', 'B6', 'B7'],
                    'C': ['C4', 'C5', 'C6', 'C7'],
                    'D': ['D4', 'D5', 'D6', 'D7']},
                   index=[4, 5, 6, 7])
pd.concat([df1, df2], axis=0)

	A	B	C	D
0	A0	B0	C0	D0
1	A1	B1	C1	D1
2	A2	B2	C2	D2
3	A3	B3	C3	D3
4	A4	B4	C4	D4
5	A5	B5	C5	D5
6	A6	B6	C6	D6
7	A7	B7	C7	D7

df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3'],
                    'C': ['C0', 'C1', 'C2', 'C3'],
                    'D': ['D0', 'D1', 'D2', 'D3']},
                   index=[0, 1, 2, 3])
df2 = pd.DataFrame({'E': ['A4', 'A5', 'A6', 'A7'],
                    'F': ['B4', 'B5', 'B6', 'B7'],
                    'G': ['C4', 'C5', 'C6', 'C7'],
                    'H': ['D4', 'D5', 'D6', 'D7']},
                   index=[0, 1, 2, 3])
pd.concat((df1, df2), axis=1)

	A	B	C	D	E	F	G	H
0	A0	B0	C0	D0	A4	B4	C4	D4
1	A1	B1	C1	D1	A5	B5	C5	D5
2	A2	B2	C2	D2	A6	B6	C6	D6
3	A3	B3	C3	D3	A7	B7	C7	D7

df1.join(df2,on=col1,how='inner') # 对数据框df1和df2做内连接，其中连接的列为col1

df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],           
                     'B': ['B0', 'B1', 'B2', 'B3'],
                     'key': ['K0', 'K1', 'K0', 'K1']})


df2 = pd.DataFrame({'C': ['C0', 'C1'],
                      'D': ['D0', 'D1']},
                     index=['K0', 'K1'])
df1.join(df2, on='key', how='inner')

	A	B	key	C	D
0	A0	B0	K0	C0	D0
2	A2	B2	K0	C0	D0
1	A1	B1	K1	C1	D1
3	A3	B3	K1	C1	D1

数据的统计

df.mean() # 得到数据框df中每一列的平均值

df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))
df.mean()

A    0.362158
B    0.432248
C    0.554478
D    0.331155
E    0.438283
dtype: float64

df.corr() # 得到数据框df中每一列与其他列的相关系数

df.corr()

	A	B	C	D	E
A	1.000000	-0.167715	0.198216	0.036939	0.113714
B	-0.167715	1.000000	0.449789	0.015883	-0.236658
C	0.198216	0.449789	1.000000	-0.296943	0.386206
D	0.036939	0.015883	-0.296943	1.000000	-0.777327
E	0.113714	-0.236658	0.386206	-0.777327	1.000000

df.count() # 得到数据框df中每一列的非空值个数

df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))
# df.loc[0][0] = np.nan
df.iloc[0, 0] = np.nan
df.count()

A     9
B    10
C    10
D    10
E    10
dtype: int64

df.max() # 得到数据框df中每一列的最大值

df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))
df.max()

A    0.812708
B    0.886171
C    0.987035
D    0.977146
E    0.959625
dtype: float64

df.min() # 得到数据框df中每一列的最小值

df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))
df.min()

A    0.128560
B    0.135905
C    0.167476
D    0.137062
E    0.050306
dtype: float64

df.median() # 得到数据框df中每一列的中位数

df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))
df.median()

A    0.409373
B    0.597418
C    0.678203
D    0.705762
E    0.519713
dtype: float64

df.std() # 得到数据框df中每一列的标准差

df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))
df.std()

A    0.272847
B    0.254870
C    0.258956
D    0.301168
E    0.295753
dtype: float64