感谢博主,转载自:
https://blog.csdn.net/qq_38328378/article/details/81166518
http://www.datastudy.cc/article/ec8c50baa8fd93ea85432eb85fb34eee
目录
技能10、根据行索引/行位置，列名/列位置，进行具体位置的值选
Pandas中行列选择的十大技能
今天，我们来学习一下，Pandas中的关于行列选择的十大技能，这些技能，绝对是你使用Pandas的过程中，需要用到的，因为，你肯定也想像Excel一样，任性地操作Python中的数据框。
先来导入我们的演示数据，这里你直接复制执行就可以了。
# import the pandas module import pandas as pd
# Create an example dataframe about a fictional army raw_data = {
'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons',
'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],
'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'],
'deaths': [523, 52, 25, 616, 43, 234, 523, 62, 62, 73, 37, 35],
'battles': [5, 42, 2, 2, 4, 7, 8, 3, 4, 7, 8, 9],
'size': [1045, 957, 1099, 1400, 1592, 1006, 987, 849, 973, 1005, 1099, 1523],
'veterans': [1, 5, 62, 26, 73, 37, 949, 48, 48, 435, 63, 345],
'readiness': [1, 2, 3, 3, 2, 1, 2, 3, 2, 1, 2, 3],
'armored': [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1],
'deserters': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],
'origin': ['Arizona', 'California', 'Texas', 'Florida', 'Maine', 'Iowa', 'Alaska', 'Washington', 'Oregon', 'Wyoming', 'Louisana', 'Georgia']
} df = pd.DataFrame(
raw_data,
columns = ['regiment', 'company', 'deaths', 'battles', 'size', 'veterans', 'readiness', 'armored', 'deserters', 'origin']
) df = df.set_index('origin') df.head()| regiment | company | deaths | battles | size | veterans | readiness | armored | deserters | |
| origin | |||||||||
| Arizona | Nighthawks | 1st | 523 | 5 | 1045 | 1 | 1 | 1 | 4 |
| California | Nighthawks | 1st | 52 | 42 | 957 | 5 | 2 | 0 | 24 |
| Texas | Nighthawks | 2nd | 25 | 2 | 1099 | 62 | 3 | 1 | 31 |
| Florida | Nighthawks | 2nd | 616 | 2 | 1400 | 26 | 3 | 1 | 2 |
| Maine | Dragoons | 1st | 43 | 4 | 1592 | 73 | 2 | 0 |
3 |
技能1、选择一列
df['size']输出结果:
origin
Arizona 1045
California 957
Texas 1099
Florida 1400
Maine 1592
Iowa 1006
Alaska 987
Washington 849
Oregon 973
Wyoming 1005
Louisana 1099
Georgia 1523
Name: size, dtype: int64技能2、选择多列
df[['size', 'veterans']]| size | veterans | |
| origin | ||
| Arizona | 1045 | 1 |
| California | 957 | 5 |
| Texas | 1099 | 62 |
| Florida | 1400 | 26 |
| Maine | 1592 | 73 |
| Iowa | 1006 | 37 |
| Alaska | 987 | 949 |
| Washington | 849 | 48 |
| Oregon | 973 | 48 |
| Wyoming | 1005 | 435 |
| Louisana | 1099 | 63 |
| Georgia | 1523 | 345 |
技能3、根据一个行索引，选择出一行
# Select all rows with the index label "Arizona"
df.loc[:'Arizona']| regiment | company | deaths | battles | size | veterans | readiness | armored | deserters | |
| origin | |||||||||
| Arizona | Nighthawks | 1st | 523 | 5 | 1045 | 1 | 1 | 1 | 4 |
技能4、根据一个行序号，选择出从开始到这个序号的行
# Select every row up to 3
df.iloc[:2]
| regiment | company | deaths | battles | size | veterans | readiness | armored | deserters | |
| origin | |||||||||
| Arizona | Nighthawks | 1st | 523 | 5 | 1045 | 1 | 1 | 1 | 4 |
| California | Nighthawks | 1st | 52 | 42 | 957 | 5 | 2 | 0 | 24 |
技能5、根据两个行序号，选择出从第一个序号到第二个序号的行
df.iloc[1:2]
| regiment | company | deaths | battles | size | veterans | readiness | armored | deserters | |
| origin | |||||||||
| California | Nighthawks | 1st | 52 | 42 | 957 | 5 | 2 | 0 | 24 |
技能6、根据一个行序号，选择出从这个行序号开始到结束的行
df.iloc[2:]| regiment | company | deaths | battles | size | veterans | readiness | armored | deserters | |
| origin | |||||||||
| Texas | Nighthawks | 2nd | 25 | 2 | 1099 | 62 | 3 | 1 | 31 |
| Florida | Nighthawks | 2nd | 616 | 2 | 1400 | 26 | 3 | 1 | 2 |
| Maine | Dragoons | 1st | 43 | 4 | 1592 | 73 | 2 | 0 | 3 |
| Iowa | Dragoons | 1st | 234 | 7 | 1006 | 37 | 1 | 1 | 4 |
| Alaska | Dragoons | 2nd | 523 | 8 | 987 | 949 | 2 | 0 | 24 |
| Washington | Dragoons | 2nd | 62 | 3 | 849 | 48 | 3 | 1 | 31 |
| Oregon | Scouts | 1st | 62 | 4 | 973 | 48 | 2 | 0 | 2 |
| Wyoming | Scouts | 1st | 73 | 7 | 1005 | 435 | 1 | 0 | 3 |
| Louisana | Scouts | 2nd | 37 | 8 | 1099 | 63 | 2 | 1 | 2 |
| Georgia | Scouts | 2nd | 35 | 9 | 1523 | 345 | 3 | 1 | 3 |
技能7、根据一个列序号，选择出从开始列到这个序号的所有列
# Select the first 2 columns
df.iloc[:,:2]| regiment | company | |
| origin | ||
| Arizona | Nighthawks | 1st |
| California | Nighthawks | 1st |
| Texas | Nighthawks | 2nd |
| Florida | Nighthawks | 2nd |
| Maine | Dragoons | 1st |
| Iowa | Dragoons | 1st |
| Alaska | Dragoons | 2nd |
| Washington | Dragoons | 2nd |
| Oregon | Scouts | 1st |
| Wyoming | Scouts | 1st |
| Louisana | Scouts | 2nd |
| Georgia | Scouts | 2nd |
技能8、条件过滤
# Select rows where df.deaths is greater than 50
df[df['deaths'] > 50]| regiment | company | deaths | battles | size | veterans | readiness | armored | deserters | |
| origin | |||||||||
| Arizona | Nighthawks | 1st | 523 | 5 | 1045 | 1 | 1 | 1 | 4 |
| California | Nighthawks | 1st | 52 | 42 | 957 | 5 | 2 | 0 | 24 |
| Florida | Nighthawks | 2nd | 616 | 2 | 1400 | 26 | 3 | 1 | 2 |
| Iowa | Dragoons | 1st | 234 | 7 | 1006 | 37 | 1 | 1 | 4 |
| Alaska | Dragoons | 2nd | 523 | 8 | 987 | 949 | 2 | 0 | 24 |
| Washington | Dragoons | 2nd | 62 | 3 | 849 | 48 | 3 | 1 | 31 |
| Oregon | Scouts | 1st | 62 | 4 | 973 | 48 | 2 | 0 | 2 |
| Wyoming | Scouts | 1st | 73 | 7 | 1005 | 435 | 1 | 0 | 3 |
# Select rows where df.deaths is greater than 500 or less than 50
df[(df['deaths'] > 500) | (df['deaths'] < 50)]| regiment | company | deaths | battles | size | veterans | readiness | armored | deserters | |
| origin | |||||||||
| Arizona | Nighthawks | 1st | 523 | 5 | 1045 | 1 | 1 | 1 | 4 |
| Texas | Nighthawks | 2nd | 25 | 2 | 1099 | 62 | 3 | 1 | 31 |
| Florida | Nighthawks | 2nd | 616 | 2 | 1400 | 26 | 3 | 1 | 2 |
| Maine | Dragoons | 1st | 43 | 4 | 1592 | 73 | 2 | 0 | 3 |
| Alaska | Dragoons | 2nd | 523 | 8 | 987 | 949 | 2 | 0 | 24 |
| Louisana | Scouts | 2nd | 37 | 8 | 1099 | 63 | 2 | 1 | 2 |
| Georgia | Scouts | 2nd | 35 | 9 | 1523 | 345 | 3 | 1 | 3 |
# Select all the regiments not named "Dragoons"
df[~(df['regiment'] == 'Dragoons')]| regiment | company | deaths | battles | size | veterans | readiness | armored | deserters | |
| origin | |||||||||
| Arizona | Nighthawks | 1st | 523 | 5 | 1045 | 1 | 1 | 1 | 4 |
| California | Nighthawks | 1st | 52 | 42 | 957 | 5 | 2 | 0 | 24 |
| Texas | Nighthawks | 2nd | 25 | 2 | 1099 | 62 | 3 | 1 | 31 |
| Florida | Nighthawks | 2nd | 616 | 2 | 1400 | 26 | 3 | 1 | 2 |
| Oregon | Scouts | 1st | 62 | 4 | 973 | 48 | 2 | 0 | 2 |
| Wyoming | Scouts | 1st | 73 | 7 | 1005 | 435 | 1 | 0 | 3 |
| Louisana | Scouts | 2nd | 37 | 8 | 1099 | 63 | 2 | 1 | 2 |
| Georgia | Scouts | 2nd | 35 | 9 | 1523 | 345 | 3 | 1 | 3 |
技能9、根据行字符串索引，进行行选择
# Select the rows called Texas and Arizona
df.ix[['Arizona', 'Texas']]
| regiment | company | deaths | battles | size | veterans | readiness | armored | deserters | |
| Arizona | Nighthawks | 1st | 523 | 5 | 1045 | 1 | 1 | 1 | 4 |
| Texas | Nighthawks | 2nd | 25 | 2 | 1099 | 62 | 3 | 1 | 31 |
技能10、根据行索引/行位置，列名/列位置，进行具体位置的值选
# Select the third cell in the row named Arizona df.ix['Arizona', 'deaths']523
# Select the third cell in the row named Arizona df.ix['Arizona', 2]
523
# Select the third cell down in the column named deaths df.ix[2, 'deaths']25