英文:
Impute missing values with group by in pandas
问题
以下是要翻译的内容：
"Having dataframe with missing values in terms of date. I need to combine all the rows into one by date column. could you please help me on this."
英文:
Having dataframe with missing values in terms of date. I need to combine all the rows into one by date column. could you please help me on this.
Input data:
Expected Intermediate Output:
After removing duplicates
答案1
得分: 1
以下是您要翻译的代码部分：
import pandas as pdimport numpy as np# 使用输入数据创建一个DataFramedata = {'PatientId': [680366, 680366, 680366, 680366, 680366, 680366, 680366, 680366, 680366],'Date': ['8/4/22 10:02', '8/4/22 10:02', '8/4/22 10:02', '8/15/22 10:04', '8/15/22 10:04','8/15/22 10:04', '10/21/22 12:19', '10/21/22 12:19', '10/21/22 12:19'],'value1': [np.nan, np.nan, np.nan, 3, np.nan, np.nan, np.nan, np.nan, np.nan],'value3': [2, np.nan, np.nan, 4, np.nan, 7, np.nan, np.nan, 7],'value4': [np.nan, 7, np.nan, np.nan, np.nan, np.nan, 4, 4, np.nan]}df = pd.DataFrame(data)# 将'Date'列转换为日期时间类型df['Date'] = pd.to_datetime(df['Date'])# 按'PatientId'和'Date'分组，并通过对每个组中的非空值求和来汇总值df_combined = df.groupby(['PatientId', 'Date']).sum(numeric_only=True, min_count=1).reset_index()print(df_combined)您可以使用相同的df来保存您的数据框。
英文:
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import pandas as pdimport numpy as np# Creating a DataFrame with the input datadata = {'PatientId': [680366, 680366, 680366, 680366, 680366, 680366, 680366, 680366, 680366],'Date': ['8/4/22 10:02', '8/4/22 10:02', '8/4/22 10:02', '8/15/22 10:04', '8/15/22 10:04','8/15/22 10:04', '10/21/22 12:19', '10/21/22 12:19', '10/21/22 12:19'],'value1': [np.nan, np.nan, np.nan, 3, np.nan, np.nan, np.nan, np.nan, np.nan],'value3': [2, np.nan, np.nan, 4, np.nan, 7, np.nan, np.nan, 7],'value4': [np.nan, 7, np.nan, np.nan, np.nan, np.nan, 4, 4, np.nan]}df = pd.DataFrame(data)# Convert the 'Date' column to datetime typedf['Date'] = pd.to_datetime(df['Date'])# Group by 'PatientId' and 'Date', and aggregate the values by summing non-null values in each groupdf_combined = df.groupby(['PatientId', 'Date']).sum(numeric_only=True, min_count=1).reset_index()print(df_combined)You can use same df for save your dataframe
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