Pandas(Python) : Fill empty cells with with previous row value?

Question

I want to fill empty cells with with previous row value if they start with number. For example, I have

    Text    Text    
    30      Text    Text    
            Text    Text    
            Text    Text    
    31      Text    Text
    Text    Text    
    31      Text    Text    
            Text    Text    
            Text    Text    
    32      Text    Text
    Text    Text    
            Text    Text    
            Text    Text    
            Text    Text    
            Text    Text

I however, want to have

Text    Text    
30      Text    Text    
30      Text    Text    
30      Text    Text    
31      Text    Text
Text    Text    
31      Text    Text    
31      Text    Text    
31      Text    Text    
32      Text    Text
Text    Text    
        Text    Text    
        Text    Text    
        Text    Text    
        Text    Text

I tried to reach this by using this code:

data = pd.read_csv('DATA.csv',sep='\t', dtype=object, error_bad_lines=False)
data = data.fillna(method='ffill', inplace=True)
print(data)

but it did not work.

Is there anyway to do this?

Answer 1

First, replace your empty cells with NaNs:

df[df[0]==""] = np.NaN

Now, Use ffill():

df.fillna(method='ffill')
#       0
#0  Text
#1    30
#2    30
#3    30
#4    31
#5  Text
#6    31
#7    31
#8    31
#9    32

Answer 2

I think you can first get NaN instead whitespaces:

df.Text = df.Text[df.Text.str.strip() != '']

print (df)
    Text Text.1      
0     30   Text  Text
1    NaN   Text  Text
2    NaN   Text  Text
3     31   Text  Text
4   Text   Text   NaN
5     31   Text  Text
6    NaN   Text  Text
7    NaN   Text  Text
8     32   Text  Text
9   Text   Text   NaN
10   NaN   Text  Text
11   NaN   Text  Text
12   NaN   Text  Text
13   NaN   Text  Text

Then use ffill (same as fillna with parameter ffill), get to_numeric for where for replace NaN if not numeric forward filling NaN, last replace NaN by empty string by fillna:

orig = df.Text.copy()
df.Text = df.Text.ffill()
mask1 = pd.to_numeric(df.Text, errors='coerce')
df.Text = df.Text.where(mask1, orig).fillna('')
print (df)
    Text Text.1      
0     30   Text  Text
1     30   Text  Text
2     30   Text  Text
3     31   Text  Text
4   Text   Text   NaN
5     31   Text  Text
6     31   Text  Text
7     31   Text  Text
8     32   Text  Text
9   Text   Text   NaN
10         Text  Text
11         Text  Text
12         Text  Text
13         Text  Text

Answer 3

In general, if you want to fill empty cells with the previous row value, you can just use a recursive function like:

def same_as_upper(col:pd.Series)-> pd.Series:
    '''
    Recursively fill NaN rows with the previous value
    '''
    if any(pd.Series(col).isna()):
        col=pd.Series(np.where(col.isna(), col.shift(1), col))
        return same_as_upper(col)
    else:
        return col

Applying the function using df['A']=same_as_upper(df['A']), this simple case with your data would render:

A	B	C
Text	Text	nan
30	Text	Text
30	Text	Text
30	Text	Text
31	Text	Text
Text	Text	nan
31	Text	Text
31	Text	Text
31	Text	Text
32	Text	Text
Text	Text	nan
Text	Text	Text
Text	Text	Text
Text	Text	Text
Text	Text	Text

However, you have the additional constraint of not replicating the previous value if this value is not an integer. In that case, one solution is add a sample value in the appropriate location, use the same function and then replace the sample value for nan:

df.loc[11,'A']=999
df['A']=same_as_upper(df['A'])
df['A']=df['A'].replace(999,np.nan)

Result:

A	B	C
Text	Text	nan
30	Text	Text
30	Text	Text
30	Text	Text
31	Text	Text
Text	Text	nan
31	Text	Text
31	Text	Text
31	Text	Text
32	Text	Text
Text	Text	nan
nan	Text	Text
nan	Text	Text
nan	Text	Text
nan	Text	Text