stack,unstack,groupby,pivot_table的区别

data=pd.DataFrame(np.arange(6).reshape((2,3)),index=pd.Index([‘A‘,‘B‘]),columns=pd.Index([‘one‘,‘two‘,‘three‘]))count()函数 列表推导式

#统计列表每个元素中指定单词出现的个数

words=[‘apple‘,‘pare‘,‘banana‘,‘and‘,‘peach‘,‘Anda‘]

for word in words:

    print(word.lower().count(‘a‘))  #lower()识别大小写

1

1

3

1

1

2

[word for word in words if word.lower().count(‘a‘)>=2]

[‘banana‘, ‘Anda‘]

strings=[‘a‘,‘bv‘,‘tit‘,‘apple‘,‘ctr‘]

[x.title() for x in strings if len(x)>2]

[‘Tit‘, ‘Apple‘, ‘Ctr‘]

list(map(len,strings))

[1, 2, 3, 5, 3]

transpose() 转置

import numpy as np

three=np.arange(18).reshape(2,3,3)

three

array([[[ 0,  1,  2],

        [ 3,  4,  5],

        [ 6,  7,  8]],

?

       [[ 9, 10, 11],

        [12, 13, 14],

        [15, 16, 17]]])

three.transpose(2,1,0)

array([[[ 0,  9],

        [ 3, 12],

        [ 6, 15]],

?

       [[ 1, 10],

        [ 4, 13],

        [ 7, 16]],

?

       [[ 2, 11],

        [ 5, 14],

        [ 8, 17]]])

?

data

dd=data.stack()

dd

dd.unstack()

dd.unstack(level=-1) #取内层索引

grouped1=df.groupby(‘key1‘)

grouped2=df.groupby([‘key1‘,‘key2‘])

[x for x in grouped1]

[x for x in grouped2]

#pandas.pivot_table(data,values=None,index=None,columns=None,aggfunc=‘mean‘,fill_value=None,margins=False,\

                   #dropna=True,margins_name=‘All‘)[source]

#pivot_table 的默认函数是mean，即求平均值。

pd.pivot_table(df,index=‘key2‘,columns=‘key1‘)

pd.pivot_table(df,index=[‘key1‘,‘key2‘])