How do you input NaN valiues in 1st row 1st column?

Question

import pandas as pd
data=['a',2,],['b',4,1],['c',6,],['d',4.4,]
df = pd.DataFrame(data, columns = ['Name', 'Age','number'])
df
    Name    Age     number
0   a   2.0     NaN
1   b   4.0     1.0
2   c   6.0     NaN
3   d   4.4     NaN

I wanted to replace "a" with NaN values (1st row 1st column)

This error shows up

data=[,2,],['b',4,1],['c',6,],['d',4.4,]
      ^
SyntaxError: invalid syntax

I have tried this instead

data=['',2,],['b',4,1],['c',6,],['d',4.4,]
df = pd.DataFrame(data, columns = ['Name', 'Age','number'])
df

    Name    Age     number
0       2.0     NaN
1   b   4.0     1.0
2   c   6.0     NaN
3   d   4.4     NaN

I then checked for missing values and obviously it is not picking up the empty values in , row1,column 1

na= [features for features in df.columns if df[features].isnull().sum()>0]
na

['number']

Any suggestions on how to navigate through this issue. Thank You

Answer 1

Pandas is built in numpy, so simply use np.nan:

import numpy as np

data=[np.nan,2,],['b',4,1],['c',6,],['d',4.4,]
df = pd.DataFrame(data, columns = ['Name', 'Age','number'])
print (df)

Ouput:

  Name  Age  number
0  NaN  2.0     NaN
1    b  4.0     1.0
2    c  6.0     NaN
3    d  4.4     NaN

Answer 2

You can simply use:

float('nan')

Answer 3

Pandas docs recommend .iat for getting / setting individual values on dataframes. Indexed by row / column pair starting from 0.

So in your case, to set column 'Name' / row 0 to numpy's nan type (imported as np below) this would be:

df.iat[0,0] = np.nan

Answer 4

you can use math module on NaN value

import pandas as pd
import math
data=[math.nan,2,],['b',4,1],['c',6,],['d',4.4,]
df = pd.DataFrame(data, columns = ['Name', 'Age','number'])
df

output

   Name Age number

0   NaN 2.0 NaN

1   b   4.0 1.0

2   c   6.0 NaN

3   d   4.4 NaN