Can't add a prefix on values based on a condition in pandas dataframe

Question

First of all, I am new to Python so I am not an expert...

Here's my issue. I have this dataframe:

   CODE_IRIS  PDL_RESIDENTIEL  PDL_TOTAL  CONSO_RESIDENTIEL  CONSO_TOTALE
0  10040101               500        510              11264         26677
1  10040102               806        809              16234         17318
2  10040201               921        925              14451         17065
3  10040202               937        943              13036         19516
4  10049999                94         94               1287          1287

The thing is, CODE_IRIS is an object and is supposed to have 9 characters, like this :

       CODE_IRIS  PDL_RESIDENTIEL  PDL_TOTAL  CONSO_RESIDENTIEL  CONSO_TOTALE
17861  766810113              588        593               9344         14743

Therefore, I need to pass a prefix 0 when the length of characters in CODE_IRIS is inferior to 9, like I would do on Excel with the formula =IF(LEN([@[Code IRIS]]) < 9; 0&[@[Code IRIS]]; [@[Code IRIS]]).

Now, when I try to locate what are the values with only 8 characters with elec.loc[elec['CODE_IRIS'].str.len() < 9], the result I get is:

Out[393]: 
Empty DataFrame
Columns: [CODE_IRIS, PDL_RESIDENTIEL, PDL_TOTAL, CONSO_RESIDENTIEL, CONSO_TOTALE]
Index: []

Then when I try to see how long each value is with elec['CODE_IRIS'].str.len(), the result I get is:

Out[396]: 
0       NaN
1       NaN
...
Name: CODE_IRIS, Length: 23905, dtype: float64

Although the column CODE_IRIS is definitely an object, as you can see here:

elec.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 23905 entries, 0 to 23904
Data columns (total 5 columns):
CODE_IRIS            23905 non-null object
PDL_RESIDENTIEL      23905 non-null int64
PDL_TOTAL            23905 non-null int64
CONSO_RESIDENTIEL    23905 non-null int64
CONSO_TOTALE         23905 non-null int64
dtypes: int64(4), object(1)
memory usage: 1.1+ MB

I don't understand. Can someone explain me what's wrong?

(I hope I have made myself as understandable as possible). thanks!

mVChr · Accepted Answer · 2019-02-11 18:48:07Z

1

You can just use zfill on every CODE_IRIS:

df['CODE_IRIS'] = df['CODE_IRIS'].map(lambda x: str(x).zfill(9))

answered Feb 11, 2019 at 18:48

mVChr

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