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. 1filename = input() Output print the output dataframe as sample. Samples sample.csv 1name,gender,age,community 2name2,F,31,F 3name5,M,12,F 4name6,F,88,E 5name3,M,18,G 6name9,F,85,D 7name5,M,87,G 8,M,26,C 9name2,F,60,F 10name2,F,89,F 11name0,F,15,E 12 Input sample.csv Output avg_age community gender D F 85.0 E F 51.5 F F 31.0 M 12.0 G M 18.0 use pandas in python
时间: 2023-12-19 07:07:43 浏览: 50
Sure, I understand your question. Here's the Python code to handle the given data as required using pandas:
```
import pandas as pd
# read the input file
filename = input("Enter the path of the file: ")
df = pd.read_csv(filename)
# handling duplicate values: keep the first one
df.drop_duplicates(subset=['name'], keep='first', inplace=True)
# handling missing values: drop the line
df.dropna(subset=['name'], inplace=True)
df['gender'].fillna(value='Unknown', inplace=True)
df['age'].fillna(value=0, inplace=True)
df['community'].fillna(value='Unknown', inplace=True)
# handling unreasonable values: fill age less than 0 with 0
df.loc[df['age'] < 0, 'age'] = 0
# group by community and gender, calculate average age
df_grouped = df.groupby(['community', 'gender']).mean()
df_output = df_grouped[['age']] # select only the 'age' column
# print the output dataframe
print(df_output)
```
This code should handle the given data as required, and output the average age of each gender in each community.
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