str attribute contains most of the methods in string.

df['ID'] = df['ID'].str.zfill(15)

See more: http://pandas.pydata.org/pandas-docs/stable/text.html

Answer from Guangyang Li on Stack Overflow
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Stack Overflow
stackoverflow.com › questions › 23836277 › add-leading-zeros-to-strings-in-pandas-dataframe
python - Add Leading Zeros to Strings in Pandas Dataframe - Stack Overflow
Top answer
1 of 8
155

str attribute contains most of the methods in string.

df['ID'] = df['ID'].str.zfill(15)

See more: http://pandas.pydata.org/pandas-docs/stable/text.html

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124

Try:

df['ID'] = df['ID'].apply(lambda x: '{0:0>15}'.format(x))

or even

df['ID'] = df['ID'].apply(lambda x: x.zfill(15))
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Top answer
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I think you can use list comprehension:

df['A'] = df['A'].apply(lambda x: [str(y).zfill(10) for y in x])
print (df)
                                                  A
0  [0011234466, 0077777777, 0012345678, 0023452345]
0  [0099999999, 0066666666, 0044332211, 0056781234]

df['A'] = [[str(y).zfill(10) for y in x] for x in df['A']]
print (df)
                                                  A
0  [0011234466, 0077777777, 0012345678, 0023452345]
0  [0099999999, 0066666666, 0044332211, 0056781234]

Similar solution with format:

df['A'] = [['{:010d}'.format(y) for y in x] for x in df['A']]
print (df)
                                                  A
0  [0011234466, 0077777777, 0012345678, 0023452345]
0  [0099999999, 0066666666, 0044332211, 0056781234]

EDIT:

data = np.array([[11234466], [77777777], [12345678], [23452345]])
data1 = np.array([[99999999], [66666666], [44332211], [56781234]])
df=pd.DataFrame({'A' : [data.tolist()]})
df1=pd.DataFrame({'A' : [data1.tolist()]})
df=pd.concat([df,df1])

df['A'] = [[['{:010d}'.format(z) for z in y] for y in x] for x in df['A']]
print (df)
                                                   A
0  [[0011234466], [0077777777], [0012345678], [00...
0  [[0099999999], [0066666666], [0044332211], [00...
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reddit.com › r/learnpython › pandas question: how can i preserve leading zeros after saving to a csv?
r/learnpython on Reddit: pandas question: how can I preserve leading zeros after saving to a CSV?
February 17, 2021 -

EDIT: Solved! Well actually there wasn't really anything to solve lol I'm just a noob and a half. To those interested in the answer, please see the reply by u/spez_edits_thedonald. Thank you to everyone who helped. Wow i love this sub so much

I have a gigantic pandas dataframe (8 million rows) and I need to upload it's contents to our platform at work. Side note: I'm kind of excited because no one in my office has ever done this on this scale, so I'm hoping I can impress my bosses.

Unfortunately, there is a file size limit of 2 MB per upload into our platform. I can't use an API, which means I don't have the option of doing this row by row directly from the pandas dataframe itself (wish I could, that would make this a lot easier). This also means that I'm not really able to manipulate the data using pandas between file upload iterations... at least I dont think so? My plan is to slice my gigantic dataframe up into a bunch of smaller CSV files (I'm pretty sure I can figure out how to do this part myself) and then do some browser automation with selenium. Selenium sounds agonizing but honestly I'm cool with letting this run all night and then waking up tomorrow to see the finished product.

The problem is: when I save it to a CSV, the leading zeros in one of my columns get dropped. This is stopping me dead in my tracks. I can't figure out how to preserve leading zeros in the CSV itself. I know how to add leading zeros in a pandas df by doing:

df['my_column'] = df['my_column'].apply(lambda x: x.zfill(5)) but this doesn't help me once it's saved to the CSV

Here is an example of my dataframe:

color shape identifier
0 blue circle 06432
1 red square 01245
2 green triangle 08750
3 yellow oval 12350
4 orange rectangle 19862

This data is good and ready to go but when I save it to a csv by doing df.to_csv('saved_df.csv') it becomes this:

color shape identifier
0 blue circle 6432
1 red square 1245
2 green triangle 8750
3 yellow oval 12350
4 orange rectangle 19862

Please note how the leading zeros in df['identifier'] are dropped

Does anyone know how to keep leading zeros within the CSV file itself? Ultimately I need the leading zeros to be there after i do df.to_csv('saved_df.csv') and when I'm uploading the CSV to our platform.

I tried doing df['identifier'] = df['identifier'].astype(str) and then saving it to a csv but it seems like that column gets converted back to integers once it is saved to the csv.

I feel like I'm so close to impressing my bosses with this sheer amount of productivity. Any help would be greatly appreciated. Thank you, kind pythonistas of reddit :)

Top answer
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The problem is: when I save it to a CSV, the leading zeros in one of my columns get dropped. How do you know? For instance, if you are opening the file in Excel, it might be that Excel is doing this trimming. Minimal example: >>> import pandas as pd >>> >>> # make a sample dataframe with numerical data >>> df = pd.DataFrame({'x': [1, 2, 300, 400, 500]}) >>> df.head() x 0 1 1 2 2 300 3 400 4 500 >>> >>> # convert the int column to str type >>> df['x'] = df['x'].astype(str) >>> >>> # add zero padding >>> df['x'] = df['x'].apply(lambda x: x.zfill(3)) >>> df.head() x 0 001 1 002 2 300 3 400 4 500 >>> >>> # save csv >>> df.to_csv('test.csv') After exiting python, I can check out the csv from terminal: $ cat test.csv ,x 0,001 1,002 2,300 3,400 4,500 the leading zeros are there. But Excel would remove them, assuming the column was numerical. So that might be your new problem to solve :) how to keep leading zeros within the CSV file itself? I bet they already ARE in the file itself I feel like I'm so close Maybe closer than you think, i.e. already done haha
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Would it be possible to change the identifier to str just to save to the csv, and changing it back when using it?