ref: Ways to filter Pandas DataFrame by column valuesFilter 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: Py
importpandasaspd# 创建一个dataframedf=pd.DataFrame({'column1':[1,51,50,100,200],'column2':['pandasdataframe.com1','pandasdataframe.com2','pandasdataframe.com3','pandasdataframe.com4','pandasdataframe.com5']})# 创建一个布尔序列bool_series=df['column1']>50# 使用布尔序列选择行filtered_df...
'Email':['tom@pandasdataframe.com','nick@pandasdataframe.com','john@pandasdataframe.com','tom@pandasdataframe.com']}df=pd.DataFrame(data,index=['a','b','c','d'])filtered_df=df.filter(items=['a','c'],axis=0)print(filtered_df)...
How do I sort a pandas DataFrame or a Series? How do I filter rows of a pandas DataFrame by column value? How do I apply multiple filter criteria to a pandas DataFrame? Your pandas questions answered! How do I use the "axis" parameter in pandas? How do I use string methods in panda...
read_csv函数,读取music.csv文件,存入变量df,此时,df为一个pandas DataFrame。 df = pandas.read_csv('music.csv') df pandas.DataFrame取列操作 此处,取第一列数据: df['Artist'] pandas.DataFrame取行操作 此处,取第二、第三行数据(⚠️注意,df[1:3]不包含左边界): df[1:3] pandas.DataFrame...
filter(items = ['actor_1_name', 'asdf'])运行时就不会出现错误，并且将返回单列的DataFrame。
方法描述DataFrame.head([n])返回前n行数据DataFrame.at快速标签常量访问器DataFrame.iat快速整型常量访问器DataFrame.loc标签定位DataFrame.iloc整型定位DataFrame.insert(loc, column, value[, …])在特殊地点插入行DataFrame.iter()Iterate over infor axisDataFrame.iteritems()返回列名和序列的迭代器DataFrame.iterrows(...
Pandas做分析数据,可以分为索引、分组、变形及合并四种操作。之前介绍过索引操作,现在接着对Pandas中的分组操作进行介绍:主要包含SAC含义、groupby函数、聚合、过滤和变换、apply函数。文章的最后,根据今天的知识介绍,给出了6个问题与2个练习,供大家学习实践。
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...
df['column_name'] # 通过标签选择数据 df.loc[row_index, column_name] # 通过位置选择数据 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...