how to round/remove traling “.0” zeros in pandas column?

Question

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         


        

Answer 1

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

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

Answer 2

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

Answer 3

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

If it is in an numberic 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', '')

Answer 4

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

Answer 5

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