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Splitting a column into 2 in a csv file using python

I have a .csv file with 100 rows of data displayed like this
"Jim 1234"
"Sam 1235"
"Mary 1236"
"John 1237"
What I'm trying to achieve is splitting the numbers from the names into 2 columns in python
edit*
Using,
import pandas as pd
df = pd.read_csv('test.csv', sep='\s+')
df.to_csv('result.csv', index=False)
I managed to get it to display like this in excel
However, the numbers still do not show up in column B as I expected.

Your data have only one column and a tab delimiter:
pd.read_csv('test.csv', quoting=1, header=None, squeeze=True) \
.str.split('\t', expand=True) \
.to_csv('result.csv', index=False, header=False)

very simple way,
data=pd.DataFrame(['Jim1234','Sam4546'])
data[0].str.split('(\d+)', expand=True)

if your file resemble to the picture below then the next code will work csv file content
import pandas as pd
df = pd.read_csv('a.csv', header=None, delimiter='\s')
df
code execution

Pandas cuts off empty columns from csv file

I have the csv file that have columns with no content just headers. And I want them to be included to resulting DataFrame but pandas cuts them off by default. Is there any way to solve this by using read_csv not read_excell?

IIUC, you need header=None:
from io import StringIO
import pandas as pd
data = """
not_header_1,not_header_2
"""
df = pd.read_csv(StringIO(data), sep=',')
print(df)
OUTPUT:
Empty DataFrame
Columns: [not_header_1, not_header_2]
Index: []
Now, with header=None
df = pd.read_csv(StringIO(data), sep=',', header=None)
print(df)
OUTPUT:
0 1
0 not_header_1 not_header_2

Python pandas xlsx/ csv

I want to convert xlsx to csv and it works, but after conversion python add ".0" to string...
Sample xlsx :
Name, Age
Mark, 20
CSV after conversion :
Name, Age
Mark, 20.0 <- add ".0"
What could the problem be?
#importing pandas as pd
import pandas as pd
# Read and store content
# of an excel file
read_file = pd.read_excel ("EXPORT.xlsx")
# Write the dataframe object
# into csv file
read_file.to_csv ("data.csv",
index = True,
header=True,
encoding='utf-8-sig')
# read csv file and convert
# into a dataframe object
df = pd.DataFrame(pd.read_csv("data.csv"))
# show the dataframe
df

I've tried to reproduce this behavior, but in my case pd.read_excel() automatically assigned the int64 format on the Age column using the presented Excel sheet.
However this case can be easily solved with the df.astype() function, that can transforms data types, e.g. for your case from floating to integer format.
#importing pandas as pd
import pandas as pd
# Read and store content
# of an excel file
read_file = pd.read_excel ("EXPORT.xlsx")
# transform data type of column "Age" to int64
read_file = read_file.astype({'Age': 'int64'})
# Write the dataframe object
# into csv file
read_file.to_csv ("data.csv",
index = True,
header=True,
encoding='utf-8-sig')
# read csv file and convert
# into a dataframe object
df = pd.DataFrame(pd.read_csv("data.csv"))
# show the dataframe
print(df)

I added float_format option and it seems that works
read_file.to_csv ("basf.csv",
index = None,
header=True,
encoding='utf-8-sig',
decimal=',',
float_format='%d'
)

pandas read csv is confused when commas within quotes

col1, col2, geometry
11.54000000,0.00000000,"{"type":"Polygon","coordinates":[[[-61.3115751786311,-33.83968838375797],[-61.29737019968823,-33.83207774370677],[-61.29443049860791,-33.83592770721248],[-61.29241347742871,-33.83489393774538],[-61.28994584513501,-33.83806650089736],[-61.292499308117186,-33.83938539699006],[-61.28958106470898,-33.8431993873636],[-61.29307859612687,-33.84495487100211],[-61.295256567865046,-33.846135537383866],[-61.296388484054326,-33.84676149889543],[-61.296747927196776,-33.84651421268175],[-61.297498943449426,-33.84670133707654],[-61.297992472179686,-33.847120134589964],[-61.299741220055196,-33.84901812154847],[-61.3012164422457,-33.85018089588664],[-61.3015892874819,-33.850566250375365],[-61.30284190607861,-33.85079121660985],[-61.30496105223345,-33.848193766906206],[-61.306084952130036,-33.84682375029292],[-61.30707604410075,-33.845532812572294],[-61.30672627175046,-33.84527169005647],[-61.306290670206494,-33.845188781884744],[-61.304604048903514,-33.847304098561025],[-61.30309763921784,-33.84654473836309],[-61.30013213880613,-33.84478736144466],[-61.30110629620797,-33.8431690707163],[-61.303046037678854,-33.844170576767105],[-61.30433047221653,-33.84266156764314],[-61.30484242472771,-33.842899106713375],[-61.30696068650711,-33.844104878773436],[-61.306418212892446,-33.84505221083753],[-61.307163201216696,-33.845464893960255],[-61.30760172622554,-33.84490909256552],[-61.307932962646014,-33.844513681420494],[-61.309176116985405,-33.84280834206188],[-61.30596211112515,-33.841126948963954],[-61.3056475423994,-33.841449215098756],[-61.30526859890979,-33.841557611902374],[-61.30483601097522,-33.84149669494795],[-61.30448925534122,-33.84120408616046],[-61.30410688411086,-33.840609953572034],[-61.30400151682434,-33.839925243738094],[-61.30240379835875,-33.83889223688216],[-61.30188418287129,-33.838444480832685],[-61.301130848179525,-33.83943255499186],[-61.30078636095504,-33.83996223583909],[-61.30059265818967,-33.84016469670277],[-61.30048478527255,-33.840438447848506],[-61.300252198180424,-33.84026774340676],[-61.29876711207748,-33.839489883020924],[-61.29799408649143,-33.840597902688785],[-61.297669258508,-33.84103160870988],[-61.297566592962134,-33.84112444052047],[-61.29748538503245,-33.841083604060834],[-61.297140578061956,-33.84134946797752],[-61.29709617977233,-33.84160419097128],[-61.297170540239335,-33.84168254110631],[-61.297341460506956,-33.84179653572337],[-61.297243418161194,-33.84197105818567],[-61.29699517169225,-33.84200300239938],[-61.29680176950715,-33.84179064473802],[-61.29691703393983,-33.8416707218475],[-61.297053755769845,-33.841604265738546],[-61.29707920124143,-33.84154875978832],[-61.29709391784669,-33.84147543150246],[-61.29711262215961,-33.84133768608576],[-61.296951411710374,-33.84119216012805],[-61.297262269660294,-33.84089514360839],[-61.297626491077864,-33.84051497848962],[-61.29865532547658,-33.83935363544152],[-61.30027710358755,-33.84011486145675],[-61.30046658230606,-33.83996490243917],[-61.30063460268783,-33.83979712050095],[-61.300992098665965,-33.8393813535522],[-61.301799802937595,-33.83832425565103],[-61.30135527704997,-33.837671541923235],[-61.30082030025984,-33.83731962483044],[-61.299512855628244,-33.83689640801839],[-61.29879550338594,-33.8363083288346],[-61.29831419490918,-33.835559835856905],[-61.298360098160686,-33.83408067231082],[-61.29976541168753,-33.83467181800819],[-61.30104200723692,-33.83586895614681],[-61.30133434017162,-33.83606352507277],[-61.30153415160492,-33.836339043812224],[-61.30164813329583,-33.83657891551336],[-61.30124575062752,-33.83743146168004],[-61.30195917352424,-33.83831965157767],[-61.30196183786503,-33.83843401993221],[-61.30250094586367,-33.83890484694379],[-61.304002690127376,-33.83984352469762],[-61.30473149692381,-33.8397514189025],[-61.3054487998093,-33.839941491549894],[-61.30582354557356,-33.84016574092716],[-61.30604808932503,-33.84046128014441],[-61.306143888278996,-33.840801374736316],[-61.30598219492593,-33.841088001849094],[-61.30757239940571,-33.841967156609876],[-61.30920555104759,-33.84277500140921],[-61.3115751786311,-33.83968838375797],[-61.3115751786311,-33.83968838375797]]]}"
How do I read a csv with syntax like above?
I am doing:
import pandas as pd
df = pd.read_csv('file.csv')
However, read_csv gets confused with the , within "{"type":"Polygon","coordinates": I want it to ignore the , within the quotes.

Your csv file contains a MultiIndex, which is causing your read and split issues.
I have tried multiple methods to read your file correctly. The best method that I have found so far is using the Python engine with an advanced separator in the read_csv function.
import pandas as pd
# these are for viewing the output
pd.set_option('display.max_columns', 30)
pd.set_option('display.max_rows', 100)
pd.set_option('display.width', 120)
# The separator matches the format of the string that you provided.
# I'm sure that it can be modified to be more efficient.
df = pd.read_csv('test.csv', skiprows=1, sep='(\d{1,2}.\d{1,8}),(\d{1,2}.\d{1,8}),("{"type":.*)',engine="python")
# some cleanup
df = df.drop(df.columns[0], axis=1)
# I had to save the processed file
df.to_csv('test_01.csv')
# read in the new file
df = pd.read_csv('test_01.csv', header=None, index_col=0)
print(df.to_string(index=False))
11.54 0.0 "{"type":"Polygon","coordinates":[[[-61.3115751786311,-33.83968838375797],[-61.29737019968823,-33.83207774370677],[-61.29443049860791,-33.83592770721248],[-61.29241347742871,-33.83489393774538],[-61.28994584513501,-33.83806650089736],[-61.292499308117186,-33.83938539699006],[-61.28958106470898,-33.8431993873636],[-61.29307859612687,-33.84495487100211],[-61.295256567865046,-33.846135537383866],[-61.296388484054326,-33.84676149889543],[-61.296747927196776,-33.84651421268175],[-61.297498943449426,-33.84670133707654],[-61.297992472179686,-33.847120134589964],[-61.299741220055196,-33.84901812154847],[-61.3012164422457,-33.85018089588664],[-61.3015892874819,-33.850566250375365],[-61.30284190607861,-33.85079121660985],[-61.30496105223345,-33.848193766906206],[-61.306084952130036,-33.84682375029292],[-61.30707604410075,-33.845532812572294],[-61.30672627175046,-33.84527169005647],[-61.306290670206494,-33.845188781884744],[-61.304604048903514,-33.847304098561025],[-61.30309763921784,-33.84654473836309],[-61.30013213880613,-33.84478736144466],[-61.30110629620797,-33.8431690707163],[-61.303046037678854,-33.844170576767105],[-61.30433047221653,-33.84266156764314],[-61.30484242472771,-33.842899106713375],[-61.30696068650711,-33.844104878773436],[-61.306418212892446,-33.84505221083753],[-61.307163201216696,-33.845464893960255],[-61.30760172622554,-33.84490909256552],[-61.307932962646014,-33.844513681420494],[-61.309176116985405,-33.84280834206188],[-61.30596211112515,-33.841126948963954],[-61.3056475423994,-33.841449215098756],[-61.30526859890979,-33.841557611902374],[-61.30483601097522,-33.84149669494795],[-61.30448925534122,-33.84120408616046],[-61.30410688411086,-33.840609953572034],[-61.30400151682434,-33.839925243738094],[-61.30240379835875,-33.83889223688216],[-61.30188418287129,-33.838444480832685],[-61.301130848179525,-33.83943255499186],[-61.30078636095504,-33.83996223583909],[-61.30059265818967,-33.84016469670277],[-61.30048478527255,-33.840438447848506],[-61.300252198180424,-33.84026774340676],[-61.29876711207748,-33.839489883020924],[-61.29799408649143,-33.840597902688785],[-61.297669258508,-33.84103160870988],[-61.297566592962134,-33.84112444052047],[-61.29748538503245,-33.841083604060834],[-61.297140578061956,-33.84134946797752],[-61.29709617977233,-33.84160419097128],[-61.297170540239335,-33.84168254110631],[-61.297341460506956,-33.84179653572337],[-61.297243418161194,-33.84197105818567],[-61.29699517169225,-33.84200300239938],[-61.29680176950715,-33.84179064473802],[-61.29691703393983,-33.8416707218475],[-61.297053755769845,-33.841604265738546],[-61.29707920124143,-33.84154875978832],[-61.29709391784669,-33.84147543150246],[-61.29711262215961,-33.84133768608576],[-61.296951411710374,-33.84119216012805],[-61.297262269660294,-33.84089514360839],[-61.297626491077864,-33.84051497848962],[-61.29865532547658,-33.83935363544152],[-61.30027710358755,-33.84011486145675],[-61.30046658230606,-33.83996490243917],[-61.30063460268783,-33.83979712050095],[-61.300992098665965,-33.8393813535522],[-61.301799802937595,-33.83832425565103],[-61.30135527704997,-33.837671541923235],[-61.30082030025984,-33.83731962483044],[-61.299512855628244,-33.83689640801839],[-61.29879550338594,-33.8363083288346],[-61.29831419490918,-33.835559835856905],[-61.298360098160686,-33.83408067231082],[-61.29976541168753,-33.83467181800819],[-61.30104200723692,-33.83586895614681],[-61.30133434017162,-33.83606352507277],[-61.30153415160492,-33.836339043812224],[-61.30164813329583,-33.83657891551336],[-61.30124575062752,-33.83743146168004],[-61.30195917352424,-33.83831965157767],[-61.30196183786503,-33.83843401993221],[-61.30250094586367,-33.83890484694379],[-61.304002690127376,-33.83984352469762],[-61.30473149692381,-33.8397514189025],[-61.3054487998093,-33.839941491549894],[-61.30582354557356,-33.84016574092716],[-61.30604808932503,-33.84046128014441],[-61.306143888278996,-33.840801374736316],[-61.30598219492593,-33.841088001849094],[-61.30757239940571,-33.841967156609876],[-61.30920555104759,-33.84277500140921],[-61.3115751786311,-33.83968838375797],[-61.3115751786311,-33.83968838375797]]]}"

Try this:
pd.read_csv('file.csv',quotechar='"',skipinitialspace=True)

Pandas Dataframe.to_csv decimal=',' doesn't work

In Python, I'm writing my Pandas Dataframe to a csv file and want to change the decimal delimiter to a comma (,). Like this:
results.to_csv('D:/Data/Kaeashi/BigData/ProcessMining/Voorbeelden/Voorbeeld/CaseEventsCel.csv', sep=';', decimal=',')
But the decimal delimiter in the csv file still is a .
Why? What do I do wrong?

If the decimal parameter doesn't work, maybe it's because the type of the column is object. (check the dtype value in the last line when you do df[column_name])
That can happen if some rows have values that couldn't be parsed as numbers.
You can force the column to change type:
Change data type of columns in Pandas.
But that can make you lose non numerical data in that column.

This functionality wasn't added until 0.16.0
Added decimal option in to_csv to provide formatting for non-‘.’ decimal separators (GH781)
Upgrade pandas to something more recent and it will work. The code below uses the 10 minute tutorial and pandas version 0.18.1
>>> import pandas as pd
>>> import numpy as np
>>> dates = pd.date_range('20130101', periods=6)
>>> df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
>>> df
A B C D
2013-01-01 -0.157833 1.719554 0.564592 -0.228870
2013-01-02 -0.316600 1.545763 -0.206499 0.793412
2013-01-03 1.905803 1.172803 0.744010 1.563306
2013-01-04 -0.142676 -0.362548 -0.554799 -0.086404
2013-01-05 1.708246 -0.505940 -1.135422 0.810446
2013-01-06 -0.150899 0.794215 -0.628903 0.598574
>>> df.to_csv("test.csv", sep=';', decimal=',')
This creates a "test.csv" file that looks like this:
;A;B;C;D
2013-01-01;-0,157833276159;1,71955439009;0,564592278787;-0,228870244247
2013-01-02;-0,316599953358;1,54576303958;-0,206499307398;0,793411528039
2013-01-03;1,90580284184;1,17280324924;0,744010110291;1,56330623177
2013-01-04;-0,142676406494;-0,36254842687;-0,554799190671;-0,0864039782679
2013-01-05;1,70824597265;-0,50594004498;-1,13542154086;0,810446051841
2013-01-06;-0,150899136973;0,794214730009;-0,628902891897;0,598573645748

In the case when data is an object, and not a plain float type, for example python decimal.Decimal(10.12). First, change a type and then write to CSV file:
import pandas as pd
from decimal import Decimal
data_frame = pd.DataFrame(data={'col1': [1.1, 2.2], 'col2': [Decimal(3.3), Decimal(4.4)]})
data_frame.to_csv('report_decimal_dot.csv', sep=';', decimal=',', float_format='%.2f')
data_frame = data_frame.applymap(lambda x: float(x) if isinstance(x, Decimal) else x)
data_frame.to_csv('report_decimal_comma.csv', sep=';', decimal=',', float_format='%.2f')

Somehow i don't get this to work either. I always just end up using the following script to rectify it. It's dirty but it works for my ends:
for col in df.columns:
try:
df[col] = df[col].apply(lambda x: float(x.replace('.','').replace(',','.')))
except:
pass
EDIT: misread the question, you might use the same tactic the other way around by changing all your floats to strings :). Then again, you should probably just figure out why it's not working. Due post it if you get it to work.

This example suppose to work (as it works for me):
import pandas as pd
import numpy as np
s = pd.Series(np.random.randn(10))
with open('Data/out.csv', 'w') as f:
s.to_csv(f, index=True, header=True, decimal=',', sep=';', float_format='%.3f')
out.csv:
;0 0;0,091 1;-0,009 2;-1,427 3;0,022 4;-1,270
5;-1,134 6;-0,965 7;-1,298 8;-0,854 9;0,150
I don't see exactly why your code doesn't work, but anyway, try to use the above example to your needs.