创建新列:使用"contains"方法创建新列。可以使用以下语法: 代码语言:txt 复制 data['new_column'] = data['string_column'].str.contains('substring') 其中,'new_column'是新列的名称,'string_column'是包含字符串的列的名称,'substring'是要检查的子字符串。
Pandas提供了多种筛选方法,包括.loc、.iloc、.query和字符串方法如str.contains()、str.startswith()等。以下是一些使用这些方法进行字符串筛选的示例: 使用.loc和str.contains()方法筛选包含特定子字符串的行: python filtered_df = df[df['your_column'].str.contains('your_substring')] 使用.query()方法...
提取字符串中的特定部分:df['column'].str.extract('(pattern)') 分割字符串列:df['column'].str.split('-') 检查字符串是否包含特定子串:df['column'].str.contains('substring') 数据输出 将数据保存为 CSV 文件:df.to_csv('new_data.csv') 将数据保存为 Excel 文件:df.to_excel('new_data.xlsx...
str.contains方法:用于检查字符串是否包含指定的子字符串。它返回一个布尔值的Series,指示每个元素是否包含给定的子字符串。语法如下: 代码语言:txt 复制 df['column_name'].str.contains(substring) 其中,'column_name'是要检查的列名,substring是要检查的子字符串。 应用场景: 关键词匹配:可以用于对文本数据进行关...
Other methods are concerned with locating substrings. Using Python'sinkeyword is the best way to detect a substring, though index and find can also be used: "guido"inval True val.index(',')# 下标索引位置 1 val.find(":")# 返回第一次出现的下标, 没有则返回 -1 ...
Relatedly,countreturns the number of occurrences of a particular substring: val.count(',') 1. replacewill substitute(替换) occurrences of one pattern for another. It is commonly used to delete patterns, too, by passing an empty string: ...
Suppose, we have a DataFrame that contains a string-type column and we need to filter the column based on a substring, if the value contains that particular substring, we need to replace the whole string. Pandas - Replacing whole string if it contains substring ...
检查(是否包含):Series.str.contains(substring)->Series(bool). 检查(是否以指定前缀/后缀开始):Series.str.startswith(prefixx)/Series.str.endswith(sufixx)->Series(bool) 检查(是否为数字字符串):Series.str.isnumeric()->Series(bool). 七、日期数据类型:这一块涉及的函数特别多,下面是几种常用的函数 ...
Now we will use Series.str.contains a () function to find if a pattern is contained in the string present in the underlying data of the given series object. Python3 # find if there is a substring such that it has # the letter 'i' followed by any small alphabet. ...
# 拆分包含特定值的列 filtered_columns = df[df['column_name'] == 'value'] # 拆分包含特定字符串的列 string_filtered_columns = df[df['column_name'].str.contains('substring')] 拆分列的数据范围: 代码语言:txt 复制 # 拆分数值列的数据范围 numeric_range_columns = df[(df['column_name'] >...