1. Dataframe转化为ndarray
  2. ndarray判断是否包含一个元素
  3. 使用Concat方法合并dataframe
  4. dataframe通过map函数进行映射
导入第三方库,以及创建公用变量(所有代码块中同名变量的类型均以此代码块为准)
    import pandas as pd
    import numpy as np
    data_df=pd.read_csv('...')
    data_array=np.array([1,2,3,])

Dataframe转化为ndarray
    #整个df转化为ndarray
    data_array=data_df.values
    #df的某一列转化为ndarray
    data_col_array=data_df['xx'].values

ndarray判断是否包含一个元素
    data_array.__contains__('ele')

使用Concat方法合并dataframe
    df1 = pd.DataFrame({
         "A": ["A0", "A1", "A2", "A3"],
         "B": ["B0", "B1", "B2", "B3"],
         "C": ["C0", "C1", "C2", "C3"],
         "D": ["D0", "D1", "D2", "D3"],},
         index=[0, 1, 2, 3],)
    df2 = pd.DataFrame({
         "A": ["A4", "A5", "A6", "A7"],
         "B": ["B4", "B5", "B6", "B7"],
         "C": ["C4", "C5", "C6", "C7"],
         "D": ["D4", "D5", "D6", "D7"],},
         index=[4, 5, 6, 7],)
    df3 = pd.DataFrame({
         "A": ["A8", "A9", "A10", "A11"],
         "B": ["B8", "B9", "B10", "B11"],
         "C": ["C8", "C9", "C10", "C11"],
         "D": ["D8", "D9", "D10", "D11"],},
         index=[8, 9, 10, 11],)
    #Concat方法
    result = pd.concat([df1, df2, df3])
result: 140x140
concat方法参数列表:
    -objs Seriesd或DataFrame的对象序列
    -axis {0,1...}拼接的方向,0表示行方向拼接,1表示列方向拼接,表示默认是0
pd.concat(
    objs,
    axis=0,
    join="outer",
    ignore_index=False,
    keys=None,
    levels=None,
    names=None,
    verify_integrity=False,
    copy=True,
)
dataframe通过map函数进行映射
    key_map={
    'a':1
    'b':2
    }
    data_df['xx']=data_df['xx'].map(key_map)