CODEWITHSUNDEEP
pythonpandas
Ptar
Ptar

Reputation: 374

How to save to_csv with python

I'd like to have these items saved to_csv as one document

df = pd.DataFrame({'STREAM':['EAGLE','HAWK','HAWK','HAWK','EAGLE','HAWK','EAGLE'],'MAT':['A','D','F','D','C','C','E'],'KIS':['B','D','E','D','A','C','D'],'GEO':['B','C','E','E','F','A','B'],'BST':['C','E','D','D','B','F','C']})

columns = ["A",'A-',"B","C","D","E", "F"]
a = df.melt(id_vars=['STREAM'], value_vars=['MAT','KIS','BST','GEO']).pivot_table(index='STREAM', columns='value', values='variable', 
 aggfunc='count', fill_value=0, margins=True, margins_name='TOT').rename_axis(None)

print('SHOW FIRST TEXT HERE')
print(a)
print()
print('SHOW SECOND TEXT HERE')
print(df)

Such that my outcome would be something like this

       A  B  C  D  E  F  TOT
EAGLE  2  4  3  1  1  1   12
HAWK   1  0  3  6  4  2   16
TOT    3  4  6  7  5  3   28

SHOW SECOND TEXT HERE
STREAM MAT KIS GEO BST
EAGLE   A   B   B   C
HAWK    D   D   C   E
HAWK    F   E   E   D
HAWK    D   D   E   D
EAGLE   C   A   F   B
HAWK    C   C   A   F
EAGLE   E   D   B   C

Anybody with leads to this to assist

Upvotes: 0

Views: 172

Answers (2)

run-out
run-out

Reputation: 3184

Saving to csv usually means saving one table with the same headers. if you want to maintain the shapes of your dataframes, saving to MS Excel is a good option, since you can save them in differents tabs or spreadsheets.

If you like, you could use pickle. This would allow you to save each of your dataframes to the pickle, perhaps in a dictionary. 

import pickle
dict = ('first': df, 'second': a)

pickle.dump(dict, open( "yourfile.pickle", "wb" ))

Then to open back up. pickle.load opens the file. In this case you stored two dataframes in one dictionary, so you will get a dictionary with two dataframes when you open/load the files from pickle. 

new_dict = pickle.load( open( "yourfile.pickle", "rb" ) )

# To then get your dataframes, 
df = new_dict['first'] 
a = new_dict['second']

HDF5 is probably more complicated than you need.

Upvotes: 1

SimonR
SimonR

Reputation: 1824

You can concatenate the two dataframes : 

pd.concat([a, df], axis=0).to_csv('concatenated_dataframes.csv')

Although the differing shapes will mean you have NaNs filling the 'gaps'

Upvotes: 1

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