100,200],'column2':['pandasdataframe.com1','pandasdataframe.com2','pandasdataframe.com3','pandasdataframe.com4','pandasdataframe.com5'],'other_column':['other1','other2','other3','other4','other5']})# 使用filter方法选择列filtered_df=df.filter(like='column')print(filtered_df)...
Filter by Column Value:To select rows based on a specific column value, use the index chain method. For example, to filter rows where sales are over 300: Pythongreater_than = df[df['Sales'] > 300] This will return rows with sales greater than 300.Filter by Multiple Conditions:...
However, you may have noticed that three values are missing in column "c" as denoted by NaN (not a number). You can filter these incomplete records from the DataFrame using .notnull() and the indexing operator: num_df[num_df['c'].notnull()] Here, you are calling.notnull()on each...
这个函数需要自己实现，函数的传入参数根据axis来定，比如axis = 1，就会把一行数据作为Series的数据 结构...
df.iloc[row_index, column_index] # 通过标签或位置选择数据 df.ix[row_index, column_name] # 选择指定的列 df.filter(items=['column_name1', 'column_name2']) # 选择列名匹配正则表达式的列 df.filter(regex='regex') # 随机选择 n 行数据 df.sample(n=5)数据...
df[df.eval("Q1 > `Q2`+@a")] 6、筛选df.filterdf.filter(items=['Q1', 'Q2']) # 选择两列 df.filter(regex='Q', axis=1) # 列名包含Q的列 df.filter(regex='e$', axis=1) # 以e结尾的列 df.filter(regex='1$', axis=0) # 正则,索引名以1结尾 ...
2.2 df.info info方法可以查看数据是否有缺失值,数据量,数据类型和数据大小等。 df2.indo() # <class 'pandas.core.frame.DataFrame'> # RangeIndex: 45 entries, 0 to 44 # Data columns (total 2 columns): # # Column Non-Null Count Dtype # --- --- --- --- # 0 math 45 non-null int6...
Filter pandas dataframe by column value Select flights details of JetBlue Airways that has 2 letters carrier code B6 with origin from JFK airport Method 1 : DataFrame Way newdf = df[(df.origin == "JFK") & (df.carrier == "B6")] ...
df.filter(items=['Q1', 'Q2']) # 选择两列 df.filter(regex='Q', axis=1) # 列名包含Q的列 df.filter(regex='e$', axis=1) # 以e结尾的列 df.filter(regex='1$', axis=0) # 正则,索引名以1结尾 df.filter(like='2', axis=0) # 索引中有2的 # 索引中以2开头、列名有Q的 df.fil...
df.filter(items=['Q1', 'Q2']) # 选择两列df.filter(regex='Q', axis=1) # 列名包含Q的列df.filter(regex='e$', axis=1) # 以e结尾的列df.filter(regex='1$', axis=0) # 正则,索引名以1结尾df.filter(like='2', axis=0) # 索引中有2的# 索引...