CODEWITHSUNDEEP
pythondataframe
Zanam
Zanam

Reputation: 4807

Python: Printing dataframe to csv

I am currently using this code:

import pandas as pd
AllDays = ['a','b','c','d']
TempDay = pd.DataFrame( np.random.randn(4,2) ) 
TempDay['Dates'] = AllDays
TempDay.to_csv('H:\MyFile.csv', index = False, header = False)

But when it prints it prints the array before the dates with a header row. I am seeking to print the dates before the TemperatureArray and no header rows.

Edit: The file is with the TemperatureArray followed by Dates: [ TemperatureArray, Date].

-0.27724356949570034,-0.3096554106726788,a
-0.10619546908708237,0.07430127684522048,b
-0.07619665345406437,0.8474460146082116,c
0.19668718143436803,-0.8072994364484335,d

I am looking to print: [ Date TemperatureArray]

a,-0.27724356949570034,-0.3096554106726788
b,-0.10619546908708237,0.07430127684522048
c,-0.07619665345406437,0.8474460146082116
d,0.19668718143436803,-0.8072994364484335

Upvotes: 0

Views: 746

Answers (1)

Alex Huszagh
Alex Huszagh

Reputation: 14634

The pandas.Dataframe.to_csv method has a keyword argument, header=True that can be turned off to disable headers.

However, it sometimes does not work (from experience). Using it in conjunction with index=False should solve your issue.

For example, this snippet should fix your issue:

TempDay.to_csv('C:\MyFile.csv', index=False, header=False)

Here is a full example showing how it disables the header row:

>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame(np.random.randn(6,4))
>>> df
          0         1         2         3 
0  1.295908  1.127376 -0.211655  0.406262 
1  0.152243  0.175974 -0.777358 -1.369432 
2  1.727280 -0.556463 -0.220311  0.474878 
3 -1.163965  1.131644 -1.084495  0.334077 
4  0.769649  0.589308  0.900430 -1.378006 
5 -2.663476  1.010663 -0.839597 -1.195599 

>>> # just assigns sequential letters to the column
>>> df[4] = [chr(i+ord('A')) for i in range(6)]
>>> df
          0         1         2         3  4
0  1.295908  1.127376 -0.211655  0.406262  A
1  0.152243  0.175974 -0.777358 -1.369432  B
2  1.727280 -0.556463 -0.220311  0.474878  C
3 -1.163965  1.131644 -1.084495  0.334077  D
4  0.769649  0.589308  0.900430 -1.378006  E
5 -2.663476  1.010663 -0.839597 -1.195599  F

>>> # here we reindex the headers and return a copy
>>> # using this form of indexing just requires you to provide
>>> # a list with all the columns you desire and in the order desired
>>> df2 = df[[4, 1, 2, 3]]
>>> df2
   4         1         2         3
0  A  1.127376 -0.211655  0.406262
1  B  0.175974 -0.777358 -1.369432
2  C -0.556463 -0.220311  0.474878
3  D  1.131644 -1.084495  0.334077
4  E  0.589308  0.900430 -1.378006
5  F  1.010663 -0.839597 -1.195599

>>> df2.to_csv('a.txt', index=False, header=False)
>>> with open('a.txt') as f:
...     print(f.read())
... 
A,1.1273756275298716,-0.21165535441591588,0.4062624848191157
B,0.17597366083826546,-0.7773584823122313,-1.3694320591723093
C,-0.556463084618883,-0.22031139982996412,0.4748783498361957
D,1.131643603259825,-1.084494967896866,0.334077296863368
E,0.5893080536600523,0.9004299653290818,-1.3780062860066293
F,1.0106633581546611,-0.839597332636998,-1.1955992812601897

If you need to dynamically adjust the columns, and move the last column to the first, you can do as follows:

# this returns the columns as a list
columns = df.columns.tolist()
# removes the last column, the newest one you added
tofirst_column = columns.pop(-1)
# just move it to the start
new_columns = [tofirst_column] + columns

# then you can the rest
df2 = df[new_columns]

This simply allows you to take the current column list, construct a Python list from the current columns, and reindex the headers without having any prior knowledge on the headers.

Upvotes: 2

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