You can use the following function to fill the NA values in a DataFrame with the average value of the row that NA belongs to:
import pandas as pd
import numpy as np
def fill_na_row(df):
df_filled = df.copy()
for index, row in df.iterrows():
avg_row = np.nanmean(row) # Calculate average of non-NaN values in the row
df_filled.loc[index] = row.fillna(avg_row) # Fill NaN values with average
return df_filled
# Sample Input
df = pd.DataFrame([[1, np.nan, 2],
[2, 3, 5],
[np.nan, 4, 6],
[None, 5, 6]])
filled_df = fill_na_row(df)
print(filled_df)
This function iterates over each row of the DataFrame and calculates the average value of non-NaN elements in that row using np.nanmean(). It then fills the NaN values in that row with this average value using fillna(). The resulting DataFrame is returned as df_filled.
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#include
#include
#include
#include
#include "sem_pv.h"
int main(void)
{
key_t key;
int pid,fd,semid,n;
char str[80];
key=ftok(".",5);
semid=sem_init(key);
fd=open("test.txt",O_...](https://linuxcpp.0voice.com/zb_users/upload/2023/05/202305162221011205155.png)
