In [5]: n =30In [6]: columns = named + np.arange(len(named), n).tolist() In [7]: df = pd.DataFrame(np.random.randn(n, n), columns=columns) In [8]: df.iloc[:, np.r_[:10,24:30]] Out[8]: a b c ...2728290-1.3443120.8448851.075770...0.8138500.132003-0.8273171-0.076...
首先进行一些设置: ```py In [140]: def extract_city_name(df): ...: """ ...: Chicago, IL -> Chicago for city_name column ...: """ ...: df["city_name"] = df["city_and_code"].str.split(",").str.get(0) ...: return df ...: In [141]: def add_country_name(df,...
DataFrame(columns=['姓名']) # 然后建立一个列表数据,列表里面是人的姓名信息 name_list = ['小李', '小张', '小五', '小六', '小七', '小八', '小九', '小十', '小高', '小马'] # 将列表名字添加到DataFrame中 df['姓名'] = name_list # 最后保存为一个新的Excel文件,文件名称为:个人信息...
2、重设Columns_name列标签: 方法一:df.columns=自定义的列名值np数组(列表) 方法二:df.rename(columns=mapper,inplace=True)等价于: df.rename(mapper : dict-like or function,axis=1,inplace=True),其中mapper :Function / dict values must be unique (1-to-1). 第二种方法思想更通用,可以发现rename(...
请注意,DataFrame 的列是一个索引,因此使用 rename_axis 与columns 参数将更改该索引的名称。 代码语言:javascript 复制 In [95]: df.rename_axis(columns="Cols").columns Out[95]: RangeIndex(start=0, stop=2, step=1, name='Cols') rename 和rename_axis 都支持指定字典、Series 或映射函数,将标签/...
Examples of selecting/excluding sets of columns in pandasNow, we will print the DataFrame by excluding some columnsExample 1# 1] Display columns according to our requirements print(df.loc[:,df.columns!='Standard']) The output of the above program is:...
Pandas allow us to achieve this task usingdf.columns.get_loc()method. This method takes the name of the column as a parameter and returns the corresponding index number. Note To work with pandas, we need to importpandaspackage first, below is the syntax: ...
mindex = pd.MultiIndex.from_product([name,month], names=['姓名',None]) df.columns = mindex #列columns转旋转成行index df1 = df.stack(level=0) df1 = df1.astype(str) #用str.extract正则提取括号内数字,也可以使用re正则模型 forcolindf1.columns: ...
result_query_sql ="SELECT table_name,table_rows FROM tables WHERE TABLE_NAME LIKE 'log%%' order by table_rows desc;" df_result = pd.read_sql(result_query_sql, engine) 生成df # list转df df_result = pd.DataFrame(pred,columns=['pred']) ...
...: columns=["first","second"], ...: ) ...: In [11]: pd.MultiIndex.from_frame(df) Out[11]: MultiIndex([('bar','one'), ('bar','two'), ('foo','one'), ('foo','two')], names=['first','second']) 作为一种便利,你可以直接将数组列表传递给Series或DataFrame以自动构建Mult...