# 用于获取带有标签列的seriesdf[column]# 选择多列df[['column_name1', 'column_name2']]# 通过标签选择单行df.loc[label] # 通过标签选择多行df.loc[[label1, label2, label3]]# 通过整数索引选择单行df.iloc[index]# 通过整数索引选择多行df.iloc[start_index:end_index]# 根据条件过滤行df[df['...
By default, TheDataFrame.drop()throwsKeyErrorif the column you are trying to delete does not exist in the dataset. If we want to drop the column only if exists then we can suppress the error by using the parametererrors. Seterrors='ignore'to not throw any errors. Seterrors='raised'to thr...
importpandasaspd# 将数据保存为CSV文件df.to_csv(, index=False)# 将数据保存为Excel文件df.to_excel(, index=False)# 将数据保存到数据库importsqlite3conn = sqlite3.connect()df.to_sql('table_name', conn, if_exists='replace', index=False)在上面的例子中，我们分别将数据保存为CSV文件、Excel文件...
import pandas as pd def test(): # 读取Excel文件 df = pd.read_excel('测试数据.xlsx') def modify_value(x): if x < 5: return '是' elif x < 10: return '否' else: return 'x' # 插入列 for col_num in range(4, 9): df.insert(loc=col_num, column=f'列{col_num-3}', value...
Series s.loc[indexer] DataFrame df.loc[row_indexer,column_indexer] 基础知识 如在上一节介绍数据结构时提到的,使用[](即__getitem__,对于熟悉在 Python 中实现类行为的人)进行索引的主要功能是选择较低维度的切片。以下表格显示了使用[]索引pandas 对象时的返回类型值: 对象类型 选择 返回值类型 Series seri...
这个方法用于选取某一列数据,其中 column_label 是列标签。第一个 “:” 表示选取所有行。 3. 选取不连续的特定行和列的数据 df.loc[row_label, column_label] 4. 选取连续的行或者列的数据(切片) df.loc[row1_label:row2_label,col1_label,col2_label] 这个方法用于选取多行多列连续的数据。 下面是...
When the column overflows, a "..." placeholder is embedded in the output. [default: 50] [currently: 200] display.max_info_columns : int max_info_columns is used in DataFrame.info method to decide if per column information will be printed. [default: 100] [currently: 100] display.max_...
index_label : str or sequence, or False, default None Column label for index column(s) if desired. If None is given, and `header` and `index` are True, then the index names are used. A sequence should be given if the object uses MultiIndex. If False do not print fields for index...
Use the drop() Method to Delete Last Column in PandasThe syntax for deleting the last n number of columns is below.df.drop( df.columns[ [ -n, ] ], axis=1, inplace=True, ) We must replace the number of columns we need to delete with the n given in the code above. If we ...
select: this creates a dropdown populated with the unique values of "column" (an asynchronous dropdown if the column has a large amount of unique values) multiselect: same as "select" but it will allow you to choose multiple values (handy if you want to perform an isin operation in your...