本文是基于Windows系統(tǒng)環(huán)境，學(xué)習(xí)和測試DataFrame模塊：

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Windows 10

PyCharm 2018.3.5 for Windows (exe)

python 3.6.8 Windows x86 executable installer

1. 初始化DataFrame

創(chuàng)建一個(gè)空的DataFrame變量

import pandas as pd

import numpy as np

data = pd.DataFrame()

print(np.shape(data)) # (0,0)

通過字典創(chuàng)建一個(gè)DataFrame

import pandas as pd

import numpy as np

dict_a = {'name': ['xu', 'wang'], 'gender': ['male', 'female']}

data = pd.DataFrame(dict_a)

print(np.shape(data)) # (2,2)

print(data)

# data =

# name gender

# 0 xu male

# 1 wang female

通過numpy.array創(chuàng)建一個(gè)DataFrame

import pandas as pd

import numpy as np

mat = np.random.randn(3,4)

df = pd.DataFrame(mat)

df.columns = ['a','b','c','d']

print(df)

一個(gè)DataFrame轉(zhuǎn)成numpy.array

import pandas as pd

import numpy as np

mat = np.random.randn(3,4)

df = pd.DataFrame(mat)

df.columns = ['a','b','c','d']

print(df)

n = np.array(df)

print(n)

DataFrame增加一列數(shù)據(jù)

import pandas as pd

import numpy as np

data = pd.DataFrame()

data['ID'] = range(0,10)

print(np.shape(data)) # (10,1)

DataFrame增加一列數(shù)據(jù)，且值相同

import pandas as pd

import numpy as np

dict_a = {'name': ['xu', 'wang'], 'gender': ['male', 'female']}

data = pd.DataFrame(dict_a)

data['country'] = 'China'

print(data)

# data =

# name gender country

# 0 xu male China

# 1 wang female China

DataFrame刪除重復(fù)的數(shù)據(jù)行

import pandas as pd

norepeat_df = df.drop_duplicates(subset=['A_ID', 'B_ID'], keep='first')

# norepeat_df = df.drop_duplicates(subset=[1, 2], keep='first')

# keep=False時(shí)，就是去掉所有的重復(fù)行

# keep=‘first'時(shí)，就是保留第一次出現(xiàn)的重復(fù)行

# keep='last'時(shí)就是保留最后一次出現(xiàn)的重復(fù)行。

2. 基本操作

去除某一列兩端的指定字符

import pandas as pd

dict_a = {'name': ['.xu', 'wang'], 'gender': ['male', 'female.']}

data = pd.DataFrame(dict_a)

print(data)

# data =

# name gender

# 0 .xu male

# 1 wang female.

data['name'] = data['name'].str.strip('.') # 刪除'.'

# data['name'] = data['name'].str.strip() # 刪除空格

print(data)

# data =

# name gender

# 0 xu male

# 1 wang female.

重新調(diào)整index的值

import pandas as pd

data = pd.DataFrame()

data['ID'] = range(0,3)

# data =

# ID

# 0 0

# 1 1

# 2 2

data.index = range(1,len(data) + 1)

# data =

# ID

# 1 0

# 2 1

# 3 2

調(diào)整DataFrame列順序

import pandas as pd

data = pd.DataFrame()

print(data)

# data =

# ID name

# 0 0 xu

# 1 1 wang

# 2 2 li

data = data[['name','ID']]

# data =

# name ID

# 0 xu 0

# 1 wang 1

# 2 li 2無錫人流醫(yī)院 http://www.bhnfkyy.com/

獲取DataFrame的列名

import pandas as pd

data = pd.DataFrame()

print(data)

# data =

# ID name

# 0 0 xu

# 1 1 wang

# 2 2 li

print(data.columns.values.tolist())

# ['ID', 'name']

獲取DataFrame的行名

import pandas as pd

data = pd.DataFrame()

print(data)

# data =

# ID name

# 0 0 xu

# 1 1 wang

# 2 2 li

print(data._stat_axis.values.tolist())

# [0, 1, 2]

3. 讀寫操作

將csv文件讀入DataFrame數(shù)據(jù)

read_csv()函數(shù)的參數(shù)配置參考官網(wǎng)pandas.read_csv

import pandas as pd

data = pd.read_csv('user.csv')

print (data)

將DataFrame數(shù)據(jù)寫入csv文件

to_csv()函數(shù)的參數(shù)配置參考官網(wǎng)pandas.DataFrame.to_csv

import pandas as pd

data = pd.read_csv('test1.csv')

data.to_csv("test2.csv",index=False, header=True)

4. 異常處理

過濾所有包含NaN的行

dropna()函數(shù)的參數(shù)配置參考官網(wǎng)pandas.DataFrame.dropna

from numpy import nan as NaN

import pandas as pd

data = pd.DataFrame([[1,2,3],[NaN,NaN,2],[NaN,NaN,NaN],[8,8,NaN]])

print (data)

# data =

# 1 2 3

# NaN NaN 2

# NaN NaN NaN

# 8 8 NaN

data = data.dropna()

# DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

# axis: 0 or 'index'表示去除行 1 or 'columns'表示去除列

# how: 'any'表示行或列只要含有NaN就去除，'all'表示行或列全都含有NaN才去除

# thresh: 整數(shù)n，表示每行或列中至少有n個(gè)元素補(bǔ)位NaN，否則去除

# subset: ['name', 'gender'] 在子集中去除NaN值，子集也可以index，但是要配合axis=1

# inplace: 如何為True，則執(zhí)行操作，然后返回None

print(data)

# data =

# 1 2 3

當(dāng)前名稱：Python中的DataFrame模塊學(xué)習(xí)
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