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()

# 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()

(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

df.loc[:,['A','B']].head() # 指定列

df.loc[[dates[0],dates[2]],['A','B']] # 指定行列

# 切片
df.loc['2013-01-01':'2013-01-04',:] # 对行切片

df.loc[:,'A':'C'].head() # 对列切片

# 切片选取连续区块。行，列。左开右闭
df.loc['2013-01-01':'2013-01-04','A':'C'] 

(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

df.iloc[:,[0,2]].head() # 指定列

df.iloc[[1,2,4],[0,2]] # 指定行列 ，先行后列

# 切片
df.iloc[1:3,:] # 对行切片:

df.iloc[:,1:3].head() # 对列切片:

df.iloc[3:5,0:2] # 切片选取连续区块。行，列。左开右闭

(2.3)Boolean索引

# 通过某列选择数据:
df[df.A > 0]

# 通过where选择数据:
b = df[df > 0]
b.head()

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()

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'])]

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()

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()

df2.iloc[1,3]

5

df2.iloc[1,3] = 10.1
df2.head()

# 通过where更新
df3 = df.copy()
df3[df3 > 0] = -df3
df3.head()

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

# 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

df1.drop(['北京', '上海'], axis = 0)

df3 = df1.drop(['北京', '上海'], axis = 0)
df3

(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

df1.drop(['2016'], axis = 1)

df1