df['Age'].fillna(value=df['Age'].mean(),inplace=True)
时间: 2023-09-09 22:11:19 浏览: 38
这段代码的作用是将数据集中的 'Age' 列中的缺失值使用该列的均值进行填充,并将填充后的结果保存到原数据集中。其中,'df' 是数据集的变量名,'Age' 是数据集中的一列, 'fillna' 是pandas库中的一个函数,用于填充缺失值,'value' 参数表示填充的值,这里使用该列的均值进行填充,'inplace=True' 表示将处理后的结果保存到原数据集中。
相关问题
import pandas as pd # #### 读取csv文件,文件路径:/data/exam/13442,文件名为:cs-training.csv data = ________1_______ # #### 查看data的行数和列数 data.__2___ # #### 查看data前10行 data.__3___ # #### 查看表的整体信息 data.__4__ # #### 查看表的均值、中位数等信息 data.__5__ # #### 查看SeriousDlqin2yrs值的分布比例 data__6_ # #### 查看data中所有列的缺失值情况 data.__7___ # #### 把MonthlyIncome根据均值填充 data['MonthlyIncome'] = data['MonthlyIncome'].____8_____ # #### 把age中小于22岁的填充为22岁,大于70岁的填充为70岁 data['age'] = data['age']._____9______ # #### 把NumberOfDependents根据-1填充 data['NumberOfDependents'] = data['NumberOfDependents'].____10_____ # #### 把age进行等宽分箱(包括6个箱子:30以下,30-39,40-49,50-59,60-69,70及以上),产生新列age_box def age_box(df): if df.age<30: return '30以下' elif 30<=df.age<=39: return '30-39' elif 40<=df.age<=49: return '40-49' elif 50<=df.age<=59: return '50-59' elif 60<=df.age<=69: return '60-69' elif 70<=df.age: return '70及以上' data['age_box'] = data.___11____ # #### 把整理好的data导出为data2.csv,不要索引 data.____12_____
以下是代码的完整填充:
```python
import pandas as pd
# 读取csv文件
data = pd.read_csv('/data/exam/13442/cs-training.csv')
# 查看data的行数和列数
print(data.shape)
# 查看data前10行
print(data.head(10))
# 查看表的整体信息
print(data.info())
# 查看表的均值、中位数等信息
print(data.describe())
# 查看SeriousDlqin2yrs值的分布比例
print(data['SeriousDlqin2yrs'].value_counts(normalize=True))
# 查看data中所有列的缺失值情况
print(data.isnull().sum())
# 把MonthlyIncome根据均值填充
data['MonthlyIncome'].fillna(data['MonthlyIncome'].mean(), inplace=True)
# 把age中小于22岁的填充为22岁,大于70岁的填充为70岁
data['age'] = data['age'].apply(lambda x: 22 if x < 22 else (70 if x > 70 else x))
# 把NumberOfDependents根据-1填充
data['NumberOfDependents'].fillna(-1, inplace=True)
# 把age进行等宽分箱(包括6个箱子:30以下,30-39,40-49,50-59,60-69,70及以上),产生新列age_box
def age_box(df):
if df.age < 30:
return '30以下'
elif 30 <= df.age <= 39:
return '30-39'
elif 40 <= df.age <= 49:
return '40-49'
elif 50 <= df.age <= 59:
return '50-59'
elif 60 <= df.age <= 69:
return '60-69'
elif 70 <= df.age:
return '70及以上'
data['age_box'] = data.apply(age_box, axis=1)
# 把整理好的data导出为data2.csv,不要索引
data.to_csv('data2.csv', index=False)
```
请注意,其中的空白处需要填写适当的代码。
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
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.