pandas DataFrame常用总结

谁借莪１个温暖的怀抱￠ 2022-12-27 11:08 195阅读 0赞

以pandas读取csv文件为例：

1.  DataFrame数据取用

import pandas
    
    df = pandas.read_csv('suites.csv',sep=',') 读取csv文件，以,分隔
    print(df)
        Status  ...                            Description
    0   passed  ...  \n            用例描述：期权强平市价委托\n        
    1   passed  ...  \n            用例描述：期权强平市价委托\n        
    2   passed  ...  \n            用例描述：期权强平市价委托\n        
    3   passed  ...  \n            用例描述：期权强平市价委托\n        
    4   passed  ...  \n            用例描述：期权强平市价委托\n        
    5   passed  ...  \n            用例描述：期权强平市价委托\n        
    6   passed  ...  \n            用例描述：期权强平市价委托\n        
    7   passed  ...                                    NaN
    8   passed  ...  \n            用例描述：期权强平市价委托\n        
    9   passed  ...                                    NaN
    10  passed  ...  \n            用例描述：期权强平市价委托\n        
    11  passed  ...  \n            用例描述：期权强平市价委托\n        
    12  passed  ...  \n            用例描述：期权强平市价委托\n        
    13  passed  ...  \n            用例描述：期权强平市价委托\n        
    14  passed  ...  \n            用例描述：期权强平市价委托\n        
    15  passed  ...  \n            用例描述：期权强平市价委托\n        
    16  passed  ...  \n            用例描述：期权强平市价委托\n        
    17  passed  ...  \n            用例描述：期权强平市价委托\n        
    18  passed  ...  \n            用例描述：期权强平市价委托\n        
    19  passed  ...                                    NaN
    20  passed  ...                                    NaN
    21  passed  ...  \n            用例描述：期权强平市价委托\n        
    
    [22 rows x 11 columns]
    print(df.head())  #打印前五行
       Status  ...                            Description
    0  passed  ...  \n            用例描述：期权强平市价委托\n        
    1  passed  ...  \n            用例描述：期权强平市价委托\n        
    2  passed  ...  \n            用例描述：期权强平市价委托\n        
    3  passed  ...  \n            用例描述：期权强平市价委托\n        
    4  passed  ...  \n            用例描述：期权强平市价委托\n        
    
    [5 rows x 11 columns]
    print(df[:3])   #打印前三行
    print(len(df.index))  #获取文件行数
    print(df.columns)   #获取文件表头
    Index(['Status', 'Start Time', 'Stop Time', 'Duration in ms', 'Parent Suite',
           'Suite', 'Sub Suite', 'Test Class', 'Test Method', 'Name',
           'Description'],
          dtype='object')
    print(df.loc[0,'Status'])  #获取Status列第0行
    print(df['Stop Time'])   #打印Stop Time列
    
    0     Mon Jul 01 20:22:29 CST 2019
    1     Mon Jul 01 20:22:25 CST 2019
    2     Mon Jul 01 20:22:25 CST 2019
    3     Mon Jul 01 20:22:31 CST 2019
    4     Mon Jul 01 20:22:25 CST 2019
    5     Mon Jul 01 20:22:25 CST 2019
    6     Mon Jul 01 20:22:31 CST 2019
    7     Mon Jul 01 20:22:33 CST 2019
    8     Mon Jul 01 20:22:31 CST 2019
    9     Mon Jul 01 20:22:31 CST 2019
    10    Mon Jul 01 20:22:25 CST 2019
    11    Mon Jul 01 20:22:31 CST 2019
    12    Mon Jul 01 20:22:25 CST 2019
    13    Mon Jul 01 20:22:25 CST 2019
    14    Mon Jul 01 20:22:25 CST 2019
    15    Mon Jul 01 20:22:31 CST 2019
    16    Mon Jul 01 20:22:31 CST 2019
    17    Mon Jul 01 20:22:31 CST 2019
    18    Mon Jul 01 20:22:25 CST 2019
    19    Mon Jul 01 20:22:25 CST 2019
    20    Mon Jul 01 20:22:25 CST 2019
    21    Mon Jul 01 20:22:31 CST 2019
    Name: Stop Time, dtype: object
    
    
    df_compare = df.compare(df2, align_axis=1)   #对比两个dataframe

1.  数据取交集、差集

df_insertaction = df.merge(df2)  #取df和df2的交集
    
    df_app = df.append(df_insertaction)
    df_app.drop_dupicates(keep=False)   #取df与df_insertaction的差集

1.  dataframe列

'A' in df.columns    #'A'是否属于df的列