I have a dataFrame like this:
id Description Price Unit
1 Test Only 1254 12
2 Data test Fresher 4
3 Sample 3569 1
4 Sample Onces Code test
5 Sample 245 2
I want to move to the left Description column from Price column if not integer then become NaN. I have no specific word to call in or match, the only thing is If Price column have Non-interger value, that string value move to Description column.
I already tried pandas replace and concat but not working.
Desire output like this:
id Description Price Unit
1 Test Only 1254 12
2 Fresher 4
3 Sample 3569 1
4 Code test
5 Sample 245 2
CodePudding user response:
This should work
# data
df = pd.DataFrame({'id': [1, 2, 3, 4, 5],
'Description': ['Test Only', 'Data test', 'Sample', 'Sample Onces', 'Sample'],
'Price': ['1254', 'Fresher', '3569', 'Code test', '245'],
'Unit': [12, 4, 1, np.nan, 2]})
# convert price column to numeric and coerce errors
price = pd.to_numeric(df.Price, errors='coerce')
# for rows where price is not numeric, replace description with these values
df.Description = df.Description.mask(price.isna(), df.Price)
# assign numeric price to price column
df.Price = price
df
CodePudding user response:
Use:
#convert valeus to numeric
price = pd.to_numeric(df['Price'], errors='coerce')
#test missing values
m = price.isna()
#shifted only matched rows
df.loc[m, ['Description','Price']] = df.loc[m, ['Description','Price']].shift(-1, axis=1)
print (df)
id Description Price
0 1 Test Only 1254
1 2 Fresher NaN
2 3 Sample 3569
3 4 Code test NaN
4 5 Sample 245
If need numeric values in ouput Price
column:
df = df.assign(Price=price)
print (df)
id Description Price
0 1 Test Only 1254.0
1 2 Fresher NaN
2 3 Sample 3569.0
3 4 Code test NaN
4 5 Sample 245.0