How to round/remove trailing ".0" zeros in pandas column?

I'm trying to see if I can remove the trailing zeros from this phone number column.

Example:

0 1 8.00735e+09 2 4.35789e+09 3 6.10644e+09 

The type in this column is an object, and I tried to round it but I am getting an error. I checked a couple of them I know they are in this format "8007354384.0", and want to get rid of the trailing zeros with the decimal point.

Sometimes I received in this format and sometimes I don't, they will be integer numbers. I would like to check if the phone column has a trailing zero, then remove it.

I have this code but I'm stuck on how to check for trailing zeros for each row.

data.ix[data.phone.str.contains('.0'), 'phone'] 

I get an error => *** ValueError: cannot index with vector containing NA / NaN values. I believe the issue is because some rows have empty data, which sometime I do receive. The code above should be able to skip an empty row.

Does anybody have any suggestions? I'm new to pandas but so far it's an useful library. Your help will be appreciated.

Note The provided example above, the first row has an empty data, which I do sometimes I get. Just to make sure this is not represented as 0 for phone number.

Also empty data is considered a string, so it's a mix of floats and string, if rows are empty.

12 Answers

use astype(np.int64)

s = pd.Series(['', 8.00735e+09, 4.35789e+09, 6.10644e+09]) mask = pd.to_numeric(s).notnull() s.loc[mask] = s.loc[mask].astype(np.int64) s 0 1 8007350000 2 4357890000 3 6106440000 dtype: object 

In Pandas/NumPy, integers are not allowed to take NaN values, and arrays/series (including dataframe columns) are homogeneous in their datatype --- so having a column of integers where some entries are None/np.nan is downright impossible.

EDIT:data.phone.astype('object') should do the trick; in this case, Pandas treats your column as a series of generic Python objects, rather than a specific datatype (e.g. str/float/int), at the cost of performance if you intend to run any heavy computations with this data (probably not in your case).

Assuming you want to keep those NaN entries, your approach of converting to strings is a valid possibility:

data.phone.astype(str).str.split('.', expand = True)[0]

should give you what you're looking for (there are alternative string methods you can use, such as .replace or .extract, but .split seems the most straightforward in this case).

Alternatively, if you are only interested in the display of floats (unlikely I'd suppose), you can do pd.set_option('display.float_format','{:.0f}'.format), which doesn't actually affect your data.

This answer by cs95 removes trailing “.0” in one row.

df = df.round(decimals=0).astype(object) 

import numpy as np import pandas as pd s = pd.Series([ None, np.nan, '',8.00735e+09, 4.35789e+09, 6.10644e+09]) s_new = s.fillna('').astype(str).str.replace(".0","",regex=False) s_new 

Here I filled null values with empty string, converted series to string type, replaced .0 with empty string.
This outputs:

0 1 2 3 8007350000 4 4357890000 5 6106440000 dtype: object 

Just do

data['phone'] = data['phone'].astype(str) data['phone'] = data['phone'].str.replace('.0', ' ') 

which uses a regex style lookup on all entries in the column and replaces any '.0' matches with blank space. For example

data = pd.DataFrame( data = [['bob','39384954.0'],['Lina','23827484.0']], columns = ['user','phone'], index = [1,2] ) data['phone'] = data['phone'].astype(str) data['phone'] = data['phone'].str.replace('.0', ' ') print data user phone 1 bob 39384954 2 Lina 23827484 

So Pandas automatically assign data type by looking at type of data in the event when you have mix type of data like some rows are NaN and some has int value there is huge possibilities it would assign dtype: object or float64

EX 1:

import pandas as pd data = [['tom', 10934000000], ['nick', 1534000000], ['juli', 1412000000]] df = pd.DataFrame(data, columns = ['Name', 'Phone']) >>> df Name Phone 0 tom 10934000000 1 nick 1534000000 2 juli 1412000000 >>> df.dtypes Name object Phone int64 dtype: object 

In above example pandas assume data type int64 reason being neither of row has NaN and all the rows in Phone column has integer value.

EX 2:

 >>> data = [['tom'], ['nick', 1534000000], ['juli', 1412000000]] >>> df = pd.DataFrame(data, columns = ['Name', 'Phone']) >>> df Name Phone 0 tom NaN 1 nick 1.534000e+09 2 juli 1.412000e+09 >>> df.dtypes Name object Phone float64 dtype: object 

To answer to your actual question, to get rid of .0 at the end you can do something like this

Solution 1:

>>> data = [['tom', 9785000000.0], ['nick', 1534000000.0], ['juli', 1412000000]] >>> df = pd.DataFrame(data, columns = ['Name', 'Phone']) >>> df Name Phone 0 tom 9.785000e+09 1 nick 1.534000e+09 2 juli 1.412000e+09 >>> df['Phone'] = df['Phone'].astype(int).astype(str) >>> df Name Phone 0 tom 9785000000 1 nick 1534000000 2 juli 1412000000 

Solution 2:

>>> df['Phone'] = df['Phone'].astype(str).str.replace('.0', '', regex=False) >>> df Name Phone 0 tom 9785000000 1 nick 1534000000 2 juli 1412000000 

Try str.isnumeric with astype and loc:

s = pd.Series(['', 8.00735e+09, 4.35789e+09, 6.10644e+09]) c = s.str.isnumeric().astype(bool) s.loc[c] = s.loc[c].astype(np.int64) print(s) 

And now:

print(s) 

Outputs:

0 1 8007350000 2 4357890000 3 6106440000 dtype: object 

Here is a solution using pandas nullable integers (the solution assumes that input Series values are either empty strings or floating point numbers):

import pandas as pd, numpy as np s = pd.Series(['', 8.00735e+09, 4.35789e+09, 6.10644e+09]) s.replace('', np.nan).astype('Int64') 

Output (pandas-0.25.1):

0 NaN 1 8007350000 2 4357890000 3 6106440000 dtype: Int64 

Advantages of the solution:

• The output values are either integers or missing values (not 'object' data type)
• Efficient

It depends on the data format the telephone number is stored.

If it is in an numeric format changing to an integer might solve the problem

df = pd.DataFrame({'TelephoneNumber': [123.0, 234]}) df['TelephoneNumber'] = df['TelephoneNumber'].astype('int32') 

If it is really a string you can replace and re-assign the column.

df2 = pd.DataFrame({'TelephoneNumber': ['123.0', '234']}) df2['TelephoneNumber'] = df2['TelephoneNumber'].str.replace('.0', '') 

import numpy as np tt = 8.00735e+09 time = int(np.format_float_positional(tt)[:-1]) 

If somebody is still interesting: I had the problem that I round the df and get the trailing zeros. Here is what I did.

new_df = np.round(old_df,3).astype(str) 

Then all trailing zeros were gone in the new_df.

I was also facing the same problem with empty rings in some rows.

The most helpful answer on this Python - Remove decimal and zero from string link helped me.