本文是基於Windows系統環境，學習和測試DataFrame模塊：
Windows 10
PyCharm 2018.3.5 for Windows (exe)
python 3.6.8 Windows x86 executable installer
1. 初始化DataFrame
創建一個空的DataFrame變量
import pandas as pd
import numpy as np
data = pd.DataFrame()
print(np.shape(data)) # (0,0)
通過字典創建一個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創建一個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DataFrame轉成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增加一列數據
import pandas as pd
import numpy as np
data = pd.DataFrame()
data['ID'] = range(0,10)
print(np.shape(data)) # (10,1)
DataFrame增加一列數據，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刪除重複的數據行
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，就是去掉所有的重複行
# keep=‘first'時，就是保留第一次出現的重複行
# keep='last'時就是保留最後一次出現的重複行。
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數據
read_csv()函數的參數配置參考官網pandas.read_csv
import pandas as pd
data = pd.read_csv('user.csv')
print (data)
將DataFrame數據寫入csv文件
to_csv()函數的參數配置參考官網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()函數的參數配置參考官網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纔去除
# thresh: 整數n，表示每行或列中至少有n個元素補位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: 如何爲True，則執行操作，然後返回None
print(data)
# data =
# 1 2 3