Replace Missing Values With Column Mean

Replace missing values with column mean

A relatively simple modification of your code should solve the issue:

for(i in 1:ncol(data)){
  data[is.na(data[,i]), i] <- mean(data[,i], na.rm = TRUE)
}

Replace missing values with the mean of each variable Python

The last few columns are strings, not floats.

Try converting to float before taking the mean:

# Make Sure Everything is numeric
num_df = num_df.apply(pd.to_numeric, errors='coerce')
# Take Mean
num_df = num_df.fillna(num_df.mean())

print(num_df)

   id    sod  pot  hemo  pcv    wc    rc
0   0  111.0  2.5  15.4   44  7800  5.20
1   1  111.0  2.5  11.3   38  6000  4.55
2   2  111.0  2.5   9.6   31  7500  4.55
3   3  111.0  2.5  11.2   32  6700  3.90

pandas DataFrame: replace nan values with average of columns

You can simply use DataFrame.fillna to fill the nan's directly:

In [27]: df 
Out[27]: 
          A         B         C
0 -0.166919  0.979728 -0.632955
1 -0.297953 -0.912674 -1.365463
2 -0.120211 -0.540679 -0.680481
3       NaN -2.027325  1.533582
4       NaN       NaN  0.461821
5 -0.788073       NaN       NaN
6 -0.916080 -0.612343       NaN
7 -0.887858  1.033826       NaN
8  1.948430  1.025011 -2.982224
9  0.019698 -0.795876 -0.046431

In [28]: df.mean()
Out[28]: 
A   -0.151121
B   -0.231291
C   -0.530307
dtype: float64

In [29]: df.fillna(df.mean())
Out[29]: 
          A         B         C
0 -0.166919  0.979728 -0.632955
1 -0.297953 -0.912674 -1.365463
2 -0.120211 -0.540679 -0.680481
3 -0.151121 -2.027325  1.533582
4 -0.151121 -0.231291  0.461821
5 -0.788073 -0.231291 -0.530307
6 -0.916080 -0.612343 -0.530307
7 -0.887858  1.033826 -0.530307
8  1.948430  1.025011 -2.982224
9  0.019698 -0.795876 -0.046431

The docstring of fillna says that value should be a scalar or a dict, however, it seems to work with a Series as well. If you want to pass a dict, you could use df.mean().to_dict().

Replace missing value with mean of class within column

Using dplyr, you could group_by Class and apply NA2mean for every column.

library(dplyr)
DF %>% group_by(class) %>% mutate_all(NA2mean)

In the newer version of dplyr, you can do this across

DF %>% group_by(class) %>% mutate(across(everything(), NA2mean))

Replace value with the average of it's column with Pandas

The first thing to recognize is the columns that have 'x' in them are not integer datatypes. They are object datatypes.

df = pd.read_csv('file.csv')

df

    Col1    Col2
0   1   22
1   2   44
2   3   x
3   4   88
4   5   110
5   6   132
6   7   x
7   8   176
8   9   198
9   10  x

df.dtypes

Col1     int64
Col2    object
dtype: object

In order to get the mean of Col2, it needs to be converted to a numeric value.

df['Col2'] = pd.to_numeric(df['Col2'], errors='coerce').astype('Int64')

df.dtypes
Col1    int64
Col2    Int64
dtype: object

The df now looks like so:

df 

Col1    Col2
0   1   22
1   2   44
2   3   <NA>
3   4   88
4   5   110
5   6   132
6   7   <NA>
7   8   176
8   9   198
9   10  <NA>

Now we can use fillna() with df['Col2'].mean():

df['Col2'] = df['Col2'].fillna(df['Col2'].mean())

df
    Col1    Col2
0   1   22
1   2   44
2   3   110
3   4   88
4   5   110
5   6   132
6   7   110
7   8   176
8   9   198
9   10  110

Replace NA in DataFrame for multiple columns with mean per country

Note Example is based on your additional data source from the comments

Replacing the NA-Values for multiple columns with mean() you can combine the following three methods:

• fillna() (Iterating per column axis should be 0, which is default value of fillna())
• groupby()
• transform()

Create data frame from your example:

df = pd.read_excel('https://happiness-report.s3.amazonaws.com/2021/DataPanelWHR2021C2.xls')

library(zoo)
df[] <- lapply(df, na.aggregate)

df2 <- df
df2[] <- lapply(df2, na.aggregate)

df2 <- replace(df, TRUE, lapply(df, na.aggregate))

ok <- sapply(df, is.numeric)
df[ok] <- lapply(df[ok], na.aggregate)

library(dplyr)
df %>%
     group_by(Day, Plate) %>%
     mutate(Value = mean(Value[!is.na(Value)]))

df %>%
     group_by(Day, Plate) %>%
     mutate(Value = mean(Value, na.rm = TRUE))