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.