利用pandas解决问题：City staff need to conduct statistics on the population information of the city, including name, gender, age, and community. There are duplicate values, missing values, and unreasonable values in the data. Please handle them as required: Duplicate values: name: if there are two names, keep the first one. Missing values: name: drop the line. gender: fill in Unknown. age: fill in 0. community: fill in Unknown. Unreasonable values: age: if the age is less than 0, fill in 0. Please output average age of each gender in each community. Input The input is the path name of the file. filename = input() Output print the output dataframe as sample.

以下是用pandas解决该问题的代码实现：

import pandas as pd

# 读取数据
filename = input()
df = pd.read_csv(filename)

# 处理重复值
df.drop_duplicates(subset=['name'], keep='first', inplace=True)

# 处理缺失值
df.dropna(subset=['name'], inplace=True)
df.fillna({'gender': 'Unknown', 'age': 0, 'community': 'Unknown'}, inplace=True)

# 处理不合理值
df['age'] = df['age'].apply(lambda x: 0 if x < 0 else x)

# 计算平均年龄
result = df.groupby(['community', 'gender']).agg({'age': 'mean'})

# 输出结果
print(result)

上述代码首先读取文件，然后依次进行重复值、缺失值和不合理值的处理。最后通过groupby函数计算每个社区、每个性别的平均年龄，并输出结果。

相关问题

write a python code:City staff need to conduct statistics on the population information of the city, including name, gender, age, and community. There are duplicate values, missing values, and unreasonable values in the data. Please handle them as required: Duplicate values: name: if there are two names, keep the first one. Missing values: name: drop the line. gender: fill in Unknown. age: fill in 0. community: fill in Unknown. Unreasonable values: age: if the age is less than 0, fill in 0. Please output average age of each gender in each community.

Here is the Python code to handle the given requirements:

import pandas as pd

# read the input data file
df = pd.read_csv('population_info.csv')

# remove duplicates based on name
df.drop_duplicates(subset=['name'], keep='first', inplace=True)

# drop rows with missing name values
df.dropna(subset=['name'], inplace=True)

# fill missing gender and community values with Unknown
df.fillna(value={'gender': 'Unknown', 'community': 'Unknown'}, inplace=True)

# fill negative age values with 0
df.loc[df['age'] < 0, 'age'] = 0

# calculate average age of each gender in each community
avg_age = df.groupby(['community', 'gender'])['age'].mean()

# print the result
print(avg_age)

Note: This code assumes that the input data file is in CSV format and is named "population_info.csv". You need to replace this with the actual file name.

帮忙写一份python代码：City staff need to conduct statistics on the population information of the city, including name, gender, age, and community. There are duplicate values, missing values, and unreasonable values in the data. Please handle them as required: Duplicate values: name: if there are two names, keep the first one. Missing values: name: drop the line. gender: fill in Unknown. age: fill in 0. community: fill in Unknown. Unreasonable values: age: if the age is less than 0, fill in 0. Please output average age of each gender in each community.

Sure, here's a possible solution:

import pandas as pd

# load data from a CSV file, assuming the columns are named "name", "gender", "age", and "community"
df = pd.read_csv("population_info.csv")

# remove duplicates based on the "name" column
df.drop_duplicates(subset=["name"], keep="first", inplace=True)

# drop rows with missing values in the "name" column
df.dropna(subset=["name"], inplace=True)

# fill missing values in the "gender" and "community" columns with "Unknown"
df.fillna({"gender": "Unknown", "community": "Unknown"}, inplace=True)

# fill negative values in the "age" column with 0
df.loc[df["age"] < 0, "age"] = 0

# group by "gender" and "community" columns, and calculate the mean age
result = df.groupby(["gender", "community"])["age"].mean()

# print the result
print(result)

This code uses the pandas library to handle data manipulation and aggregation. It first removes duplicate values based on the "name" column, then drops rows with missing values in the "name" column. It fills missing values in the "gender" and "community" columns with "Unknown", and fills negative values in the "age" column with 0. Finally, it groups the data by "gender" and "community", and calculates the mean age for each group. The result is printed to the console. Note that you may need to adjust the code according to the format and structure of your input data.