df.loc[(df['class'].str.contains('7|8')) & (df['number of classes'] >= 2), 'result'] = 1 修改这段代码，变成包含7和8

要将代码修改为仅包含数字7和8的字符串，您可以使用`str.contains`函数的正则表达式模式，如下所示：

import re

df.loc[(df['class'].str.contains('[78]')) & (df['number of classes'] >= 2), 'result'] = 1

在正则表达式模式`'[78]'`中，方括号`[]`表示匹配方括号内的任何一个字符。因此，该模式将匹配数字7和8。

相关问题

df = df.loc[df['exchange'].str.startswith(('A'))] df = df.loc[~df['ts_code'].str.startswith(('882'))] 合并成一句

可以使用逻辑运算符 "&" 来合并这两句代码，如下所示：

df = df.loc[df['exchange'].str.startswith(('A')) & ~df['ts_code'].str.startswith(('882'))]

这样就可以同时筛选出 `exchange` 列以 "A" 开头且 `ts_code` 列不以 "882" 开头的行。

解释import pandas as pd df = pd.read_csv('S12_wearther_central_park.csv') df['DATE'] = pd.to_datetime(df['DATE']) df.set_index('DATE', inplace=True) x = input() year_df = df.loc[str(x), ['PRCP', 'TMIN', 'TMAX']] rainy_days = year_df[year_df['PRCP'] > 1.3] print(rainy_days)

Certainly! Let me explain what each line of the code does:

import pandas as pd

This line imports the Pandas library and assigns it the alias `pd`, which is commonly used in Python code.

df = pd.read_csv('S12_wearther_central_park.csv')

This line reads the CSV file 'S12_wearther_central_park.csv' into a Pandas DataFrame called `df`. The data in the CSV file is assumed to be comma-separated.

df['DATE'] = pd.to_datetime(df['DATE'])

This line converts the 'DATE' column of the DataFrame to a Pandas datetime object. This allows us to perform various operations on the date, such as filtering by year or month.

df.set_index('DATE', inplace=True)

This line sets the 'DATE' column as the index of the DataFrame. This is useful for quickly accessing data based on the date.

x = input()

This line prompts the user for input and assigns it to the variable `x`. This input is assumed to be a year in the format of a string, e.g. '2010'.

year_df = df.loc[str(x), ['PRCP', 'TMIN', 'TMAX']]

This line creates a new DataFrame called `year_df` that contains the precipitation, minimum temperature, and maximum temperature data for the year specified by the user input. The `.loc` method is used to slice the DataFrame by the year, and the square brackets are used to select the columns of interest.

rainy_days = year_df[year_df['PRCP'] > 1.3]

This line creates a new DataFrame called `rainy_days` that contains only the rows of `year_df` where the precipitation value is greater than 1.3 inches. This is done by using boolean indexing and comparing the 'PRCP' column to the value 1.3.

print(rainy_days)

This line prints the `rainy_days` DataFrame to the console. This DataFrame contains the date, precipitation, minimum temperature, and maximum temperature for the days where precipitation was greater than 1.3 inches.