Pandas基础-DataFrame-增删改查

其他 2018-10-30 17:12:30 阅读次数: 0
版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/QianYanDai/article/details/78168944

DataFrame查增改删


一、查 Read

(1)类list/ndarray数据访问方式

import pandas as pd

dates = pd.date_range('20130101',periods=10)
dates
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
               '2013-01-05', '2013-01-06', '2013-01-07', '2013-01-08',
               '2013-01-09', '2013-01-10'],
              dtype='datetime64[ns]', freq='D')

import numpy as np

df = pd.DataFrame(np.random.randn(10,4),index=dates,columns=['A','B','C','D'])
df.head()
A B C D
2013-01-01 -0.094887 -1.334026 -0.191152 -0.161571
2013-01-02 0.699451 1.864515 -0.826253 0.630770
2013-01-03 -0.706805 0.829414 -1.758934 -1.512252
2013-01-04 -0.159045 0.130182 1.089689 0.024567
2013-01-05 -1.971381 0.848481 0.823581 0.541739 
# 索引
df['A'].head()
2013-01-01   -0.094887
2013-01-02    0.699451
2013-01-03   -0.706805
2013-01-04   -0.159045
2013-01-05   -1.971381
Freq: D, Name: A, dtype: float64

df.A.head()
2013-01-01   -0.094887
2013-01-02    0.699451
2013-01-03   -0.706805
2013-01-04   -0.159045
2013-01-05   -1.971381
Freq: D, Name: A, dtype: float64

df['A']['2013-01-01'] # 先列后行
0.75407705661157032

df.A['2013-01-01']
0.75407705661157032

df[['A','C']].head()
A C
2013-01-01 -0.094887 -0.191152
2013-01-02 0.699451 -0.826253
2013-01-03 -0.706805 -1.758934
2013-01-04 -0.159045 1.089689
2013-01-05 -1.971381 0.823581

(2)Pandas专用的数据访问方式

pandas的索引函数主要有三种：

loc 标签索引，行和列的名称

iloc 整型索引（绝对位置索引），绝对意义上的几行几列，起始索引为0

ix 是 iloc 和 loc的合体

at是loc的快捷方式

iat是iloc的快捷方式

(2.1).loc 通过自定义索引获取数据

# 选取某行
df.loc['2013-01-01']
A   -0.094887
B   -1.334026
C   -0.191152
D   -0.161571
Name: 2013-01-01 00:00:00, dtype: float64

# 选取某列
df.loc[:,'A'].head()
2013-01-01   -0.094887
2013-01-02    0.699451
2013-01-03   -0.706805
2013-01-04   -0.159045
2013-01-05   -1.971381
Freq: D, Name: A, dtype: float64

# 选取特定值
df.loc['2013-01-01','A'] # 先行后列
0.75407705661157032

# 选取指定的行/列
df.loc[[dates[0],dates[2]],:] # 指定行
A B C D
2013-01-01 0.754077 -0.346202 -0.557050 0.778106
2013-01-03 0.174730 2.056007 1.781379 1.643397 
df.loc[:,['A','B']].head() # 指定列
A B
2013-01-01 -0.094887 -1.334026
2013-01-02 0.699451 1.864515
2013-01-03 -0.706805 0.829414
2013-01-04 -0.159045 0.130182
2013-01-05 -1.971381 0.848481 
df.loc[[dates[0],dates[2]],['A','B']] # 指定行列
A B
2013-01-01 0.754077 -0.346202
2013-01-03 0.174730 2.056007 
# 切片
df.loc['2013-01-01':'2013-01-04',:] # 对行切片
A B C D
2013-01-01 0.754077 -0.346202 -0.557050 0.778106
2013-01-02 0.103394 -1.051044 -0.413054 0.268955
2013-01-03 0.174730 2.056007 1.781379 1.643397
2013-01-04 -0.950517 -0.226887 -0.097138 -0.442010 
df.loc[:,'A':'C'].head() # 对列切片
A B C
2013-01-01 -0.094887 -1.334026 -0.191152
2013-01-02 0.699451 1.864515 -0.826253
2013-01-03 -0.706805 0.829414 -1.758934
2013-01-04 -0.159045 0.130182 1.089689
2013-01-05 -1.971381 0.848481 0.823581 
# 切片选取连续区块。行，列。左开右闭
df.loc['2013-01-01':'2013-01-04','A':'C']
A B C
2013-01-01 0.754077 -0.346202 -0.557050
2013-01-02 0.103394 -1.051044 -0.413054
2013-01-03 0.174730 2.056007 1.781379
2013-01-04 -0.950517 -0.226887 -0.097138

(2.2).iloc 通过默认索引获取数据

# 选取某行
df.iloc[3]
A   -0.950517
B   -0.226887
C   -0.097138
D   -0.442010
Name: 2013-01-04 00:00:00, dtype: float64

# 选取某列
df.iloc[:,2].head()
2013-01-01   -0.191152
2013-01-02   -0.826253
2013-01-03   -1.758934
2013-01-04    1.089689
2013-01-05    0.823581
Freq: D, Name: C, dtype: float64

# 选取特定值:
df.iloc[1,2]
-0.41305425875508139

# 选取指定的行/列
df.iloc[[1,2,4],:] # 指定行
A B C D
2013-01-02 0.103394 -1.051044 -0.413054 0.268955
2013-01-03 0.174730 2.056007 1.781379 1.643397
2013-01-05 0.076178 -0.518970 1.142290 -0.952401 
df.iloc[:,[0,2]].head() # 指定列
A C
2013-01-01 -0.094887 -0.191152
2013-01-02 0.699451 -0.826253
2013-01-03 -0.706805 -1.758934
2013-01-04 -0.159045 1.089689
2013-01-05 -1.971381 0.823581 
df.iloc[[1,2,4],[0,2]] # 指定行列 ，先行后列
A C
2013-01-02 0.103394 -0.413054
2013-01-03 0.174730 1.781379
2013-01-05 0.076178 1.142290 
# 切片
df.iloc[1:3,:] # 对行切片:
A B C D
2013-01-02 0.103394 -1.051044 -0.413054 0.268955
2013-01-03 0.174730 2.056007 1.781379 1.643397 
df.iloc[:,1:3].head() # 对列切片:
B C
2013-01-01 -1.334026 -0.191152
2013-01-02 1.864515 -0.826253
2013-01-03 0.829414 -1.758934
2013-01-04 0.130182 1.089689
2013-01-05 0.848481 0.823581 
df.iloc[3:5,0:2] # 切片选取连续区块。行，列。左开右闭
A B
2013-01-04 -0.950517 -0.226887
2013-01-05 0.076178 -0.518970

(2.3)Boolean索引

# 通过某列选择数据:
df[df.A > 0]
A B C D
2013-01-02 0.699451 1.864515 -0.826253 0.630770
2013-01-07 0.117383 0.103409 -1.039062 1.929632
2013-01-10 1.109493 -0.708581 0.284196 0.938002 
# 通过where选择数据:
b = df[df > 0]
b.head()
A B C D
2013-01-01 NaN NaN NaN NaN
2013-01-02 0.699451 1.864515 NaN 0.630770
2013-01-03 NaN 0.829414 NaN NaN
2013-01-04 NaN 0.130182 1.089689 0.024567
2013-01-05 NaN 0.848481 0.823581 0.541739 
type(b['A']['2013-01-01'])
numpy.float64

# 通过 isin() 过滤数据:
df2 = df.copy()
df2['E'] = ['one', 'one','two','three','four','three','five','four','three','five']
df2.head()
A B C D E
2013-01-01 -0.094887 -1.334026 -0.191152 -0.161571 one
2013-01-02 0.699451 1.864515 -0.826253 0.630770 one
2013-01-03 -0.706805 0.829414 -1.758934 -1.512252 two
2013-01-04 -0.159045 0.130182 1.089689 0.024567 three
2013-01-05 -1.971381 0.848481 0.823581 0.541739 four 
df2['E'].isin(['one','four']).head()
2013-01-01     True
2013-01-02     True
2013-01-03    False
2013-01-04    False
2013-01-05     True
Freq: D, Name: E, dtype: bool

df2[df2['E'].isin(['one','four'])]
A B C D E
2013-01-01 0.754077 -0.346202 -0.557050 0.778106 one
2013-01-02 0.103394 -1.051044 -0.413054 0.268955 one
2013-01-05 0.076178 -0.518970 1.142290 -0.952401 four
2013-01-08 -1.246918 1.530266 1.761499 0.940741 four


二、增 Create

s1 = pd.Series([1,2,3,4,5,6], 
               index=pd.date_range('20130102', periods=6))
s1
2013-01-02    1
2013-01-03    2
2013-01-04    3
2013-01-05    4
2013-01-06    5
2013-01-07    6
Freq: D, dtype: int64

# 新增一列数据
df2['F'] = s1
df2.head()
A B C D E F
2013-01-01 -0.094887 -1.334026 -0.191152 -0.161571 one NaN
2013-01-02 0.699451 1.864515 -0.826253 0.630770 one 1.0
2013-01-03 -0.706805 0.829414 -1.758934 -1.512252 two 2.0
2013-01-04 -0.159045 0.130182 1.089689 0.024567 three 3.0
2013-01-05 -1.971381 0.848481 0.823581 0.541739 four 4.0

三、改 Update

# 更新一列值
df2.loc[:,'D'].head()
2013-01-01   -0.161571
2013-01-02    0.630770
2013-01-03   -1.512252
2013-01-04    0.024567
2013-01-05    0.541739
Freq: D, Name: D, dtype: float64

df2.loc[:,'D'] = 5
df2.head()
A B C D E F
2013-01-01 -0.094887 -1.334026 -0.191152 5 one NaN
2013-01-02 0.699451 1.864515 -0.826253 5 one 1.0
2013-01-03 -0.706805 0.829414 -1.758934 5 two 2.0
2013-01-04 -0.159045 0.130182 1.089689 5 three 3.0
2013-01-05 -1.971381 0.848481 0.823581 5 four 4.0 
df2.iloc[1,3]
5

df2.iloc[1,3] = 10.1
df2.head()
A B C D E F
2013-01-01 -0.094887 -1.334026 -0.191152 5.0 one NaN
2013-01-02 0.699451 1.864515 -0.826253 10.1 one 1.0
2013-01-03 -0.706805 0.829414 -1.758934 5.0 two 2.0
2013-01-04 -0.159045 0.130182 1.089689 5.0 three 3.0
2013-01-05 -1.971381 0.848481 0.823581 5.0 four 4.0 
# 通过where更新
df3 = df.copy()
df3[df3 > 0] = -df3
df3.head()
A B C D
2013-01-01 -0.094887 -1.334026 -0.191152 -0.161571
2013-01-02 -0.699451 -1.864515 -0.826253 -0.630770
2013-01-03 -0.706805 -0.829414 -1.758934 -1.512252
2013-01-04 -0.159045 -0.130182 -1.089689 -0.024567
2013-01-05 -1.971381 -0.848481 -0.823581 -0.541739

四、删 drop

(1)删除行

import pandas as pd
import numpy as np

df1 = pd.DataFrame(np.arange(1, 17).reshape(4, 4),
            index = ['北京', '上海', '广州', '深圳'],
            columns = ['2015', '2016', '2017', '2018'])

df1
2015 2016 2017 2018
北京 1 2 3 4
上海 5 6 7 8
广州 9 10 11 12
深圳 13 14 15 16 
# 需要重新创建一个对象并赋值，不然原数据并不会改变，只是不会显示需要删除的行
df2 = df1.drop(['北京'])

df2
2015 2016 2017 2018
上海 5 6 7 8
广州 9 10 11 12
深圳 13 14 15 16 
df1.drop(['北京', '上海'], axis = 0)
2015 2016 2017 2018
广州 9 10 11 12
深圳 13 14 15 16 
df3 = df1.drop(['北京', '上海'], axis = 0)
df3
2015 2016 2017 2018
广州 9 10 11 12
深圳 13 14 15 16

(2)删除列

1）直接del DF[‘column-name’]

2）采用drop方法，有下面三种等价的表达式：

DF= DF.drop(‘column_name’, 1)；

DF.drop(‘column_name’,axis=1, inplace=True)

DF.drop([DF.columns[[0,1,]]], axis=1,inplace=True)

# 直接del DF[‘column-name’]
del df1['2015']
df1
2016 2017 2018
北京 2 3 4
上海 6 7 8
广州 10 11 12
深圳 14 15 16 
df1.drop(['2016'], axis = 1)
2017 2018
北京 3 4
上海 7 8
广州 11 12
深圳 15 16 
df1
2016 2017 2018
北京 2 3 4
上海 6 7 8
广州 10 11 12
深圳 14 15 16

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