精选23个Pandas常用函数

从26个字母中精选出23个Pandas常用的函数，将它们的使用方法介绍给大家。其中o、y、z没有相应的函数。

import pandas as pd
import numpy as np

下面介绍每个函数的使用方法，更多详细的内容请移步官网：pandas.pydata.org/docs/refere…

assign函数

df = pd.DataFrame({
    'temp_c': [17.0, 25.0]},
    index=['Portland', 'Berkeley'])
df

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	temp_c
Portland	17.0
Berkeley	25.0

# 生成新的字段

df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)

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	temp_c	temp_f
Portland	17.0	62.6
Berkeley	25.0	77.0

df  # 原来DataFrame是不改变的

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	temp_c
Portland	17.0
Berkeley	25.0

df["temp_f1"] = df["temp_c"] * 9 / 5 + 32
df

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	temp_c	temp_f1
Portland	17.0	62.6
Berkeley	25.0	77.0

df

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	temp_c	temp_f1
Portland	17.0	62.6
Berkeley	25.0	77.0

bool函数

返回单个Series或者DataFrame中单个元素的bool值：True或者False

pd.Series([True]).bool()

True

pd.Series([False]).bool()

False

pd.DataFrame({'col': [True]}).bool()

True

pd.DataFrame({'col': [False]}).bool()

False

# # 多个元素引发报错

# pd.DataFrame({'col': [True,False]}).bool()

concat函数

该函数是用来表示多个DataFrame的拼接，横向或者纵向皆可。

df1 = pd.DataFrame({
    "sid":["s1","s2"],
    "name":["xiaoming","Mike"]})
df1

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	sid	name
0	s1	xiaoming
1	s2	Mike

df2 = pd.DataFrame({
    "sid":["s3","s4"],
    "name":["Tom","Peter"]})
df2

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	sid	name
0	s3	Tom
1	s4	Peter

df3 = pd.DataFrame({
    "address":["北京","深圳"],             
    "sex":["Male","Female"]})
df3

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	address	sex
0	北京	Male
1	深圳	Female

# 使用1：纵向
pd.concat([df1,df2])

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	sid	name
0	s1	xiaoming
1	s2	Mike
0	s3	Tom
1	s4	Peter

# 使用2：横向
pd.concat([df1,df3],axis=1)

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	sid	name	address	sex
0	s1	xiaoming	北京	Male
1	s2	Mike	深圳	Female

dropna函数

删除空值

df4 = pd.DataFrame({
    "sid":["s1","s2", np.nan],             
    "name":["xiaoming",np.nan, "Mike"]})
df4

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	sid	name
0	s1	xiaoming
1	s2	NaN
2	NaN	Mike

df4.dropna()

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	sid	name
0	s1	xiaoming

df4.dropna(subset=["name"])

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	sid