Python, Pandas : write content of DataFrame into text File
Question:
I have pandas DataFrame like this
X Y Z Value
0 18 55 1 70
1 18 55 2 67
2 18 57 2 75
3 18 58 1 35
4 19 54 2 70
I want to write this data to a text file that looks like this:
18 55 1 70
18 55 2 67
18 57 2 75
18 58 1 35
19 54 2 70
I have tried something like
f = open(writePath, 'a')
f.writelines(['n', str(data['X']), ' ', str(data['Y']), ' ', str(data['Z']), ' ', str(data['Value'])])
f.close()
It’s not correct. How to do this?
Answers:
You can just use np.savetxt and access the np attribute .values:
np.savetxt(r'c:datanp.txt', df.values, fmt='%d')
yields:
18 55 1 70
18 55 2 67
18 57 2 75
18 58 1 35
19 54 2 70
or to_csv:
df.to_csv(r'c:datapandas.txt', header=None, index=None, sep=' ', mode='a')
Note for np.savetxt you’d have to pass a filehandle that has been created with append mode.
You can use pandas.DataFrame.to_csv(), and setting both index and header to False:
In [97]: print df.to_csv(sep=' ', index=False, header=False)
18 55 1 70
18 55 2 67
18 57 2 75
18 58 1 35
19 54 2 70
pandas.DataFrame.to_csv can write to a file directly, for more info you can refer to the docs linked above.
Late to the party: Try this>
base_filename = 'Values.txt'
with open(os.path.join(WorkingFolder, base_filename),'w') as outfile:
df.to_string(outfile)
#Neatly allocate all columns and rows to a .txt file
The native way to do this is to use df.to_string() :
with open(writePath, 'a') as f:
dfAsString = df.to_string(header=False, index=False)
f.write(dfAsString)
Will output the following
18 55 1 70
18 55 2 67
18 57 2 75
18 58 1 35
19 54 2 70
This method also lets you easily choose which columns to print with the columns attribute, lets you keep the column, index labels if you wish, and has other attributes for spacing ect.
@AHegde – To get the tab delimited output use separator sep=’t’.
For df.to_csv:
df.to_csv(r'c:datapandas.txt', header=None, index=None, sep='t', mode='a')
For np.savetxt:
np.savetxt(r'c:datanp.txt', df.values, fmt='%d', delimiter='t')
I used a slightly modified version:
with open(file_name, 'w', encoding = 'utf-8') as f:
for rec_index, rec in df.iterrows():
f.write(rec['<field>'] + 'n')
I had to write the contents of a dataframe field (that was delimited) as a text file.
Way to get Excel data to text file in tab delimited form.
Need to use Pandas as well as xlrd.
import pandas as pd
import xlrd
import os
Path="C:downloads"
wb = pd.ExcelFile(Path+"\input.xlsx", engine=None)
sheet2 = pd.read_excel(wb, sheet_name="Sheet1")
Excel_Filter=sheet2[sheet2['Name']=='Test']
Excel_Filter.to_excel("C:downloads\output.xlsx", index=None)
wb2=xlrd.open_workbook(Path+"\output.xlsx")
df=wb2.sheet_by_name("Sheet1")
x=df.nrows
y=df.ncols
for i in range(0,x):
for j in range(0,y):
A=str(df.cell_value(i,j))
f=open(Path+"\emails.txt", "a")
f.write(A+"t")
f.close()
f=open(Path+"\emails.txt", "a")
f.write("n")
f.close()
os.remove(Path+"\output.xlsx")
print(Excel_Filter)
We need to first generate the xlsx file with filtered data and then convert the information into a text file.
Depending on requirements, we can use n t for loops and type of data we want in the text file.
If you have a Dataframe that is an output of pandas compare method, such a dataframe looks like below when it is printed:
grossRevenue netRevenue defaultCost
self other self other self other
2098 150.0 160.0 NaN NaN NaN NaN
2110 1400.0 400.0 NaN NaN NaN NaN
2127 NaN NaN NaN NaN 0.0 909.0
2137 NaN NaN 0.000000 8.900000e+01 NaN NaN
2150 NaN NaN 0.000000 8.888889e+07 NaN NaN
2162 NaN NaN 1815.000039 1.815000e+03 NaN NaN
I was looking to persist the whole dataframe into a text file as its visible above. Using pandas’s to_csv or numpy’s savetxt does not achieve this goal. I used plain old print to log the same into a text file:
with open('file1.txt', mode='w') as file_object:
print(data_frame, file=file_object)
I have pandas DataFrame like this
X Y Z Value
0 18 55 1 70
1 18 55 2 67
2 18 57 2 75
3 18 58 1 35
4 19 54 2 70
I want to write this data to a text file that looks like this:
18 55 1 70
18 55 2 67
18 57 2 75
18 58 1 35
19 54 2 70
I have tried something like
f = open(writePath, 'a')
f.writelines(['n', str(data['X']), ' ', str(data['Y']), ' ', str(data['Z']), ' ', str(data['Value'])])
f.close()
It’s not correct. How to do this?
You can just use np.savetxt and access the np attribute .values:
np.savetxt(r'c:datanp.txt', df.values, fmt='%d')
yields:
18 55 1 70
18 55 2 67
18 57 2 75
18 58 1 35
19 54 2 70
or to_csv:
df.to_csv(r'c:datapandas.txt', header=None, index=None, sep=' ', mode='a')
Note for np.savetxt you’d have to pass a filehandle that has been created with append mode.
You can use pandas.DataFrame.to_csv(), and setting both index and header to False:
In [97]: print df.to_csv(sep=' ', index=False, header=False)
18 55 1 70
18 55 2 67
18 57 2 75
18 58 1 35
19 54 2 70
pandas.DataFrame.to_csv can write to a file directly, for more info you can refer to the docs linked above.
Late to the party: Try this>
base_filename = 'Values.txt'
with open(os.path.join(WorkingFolder, base_filename),'w') as outfile:
df.to_string(outfile)
#Neatly allocate all columns and rows to a .txt file
The native way to do this is to use df.to_string() :
with open(writePath, 'a') as f:
dfAsString = df.to_string(header=False, index=False)
f.write(dfAsString)
Will output the following
18 55 1 70
18 55 2 67
18 57 2 75
18 58 1 35
19 54 2 70
This method also lets you easily choose which columns to print with the columns attribute, lets you keep the column, index labels if you wish, and has other attributes for spacing ect.
@AHegde – To get the tab delimited output use separator sep=’t’.
For df.to_csv:
df.to_csv(r'c:datapandas.txt', header=None, index=None, sep='t', mode='a')
For np.savetxt:
np.savetxt(r'c:datanp.txt', df.values, fmt='%d', delimiter='t')
I used a slightly modified version:
with open(file_name, 'w', encoding = 'utf-8') as f:
for rec_index, rec in df.iterrows():
f.write(rec['<field>'] + 'n')
I had to write the contents of a dataframe field (that was delimited) as a text file.
Way to get Excel data to text file in tab delimited form.
Need to use Pandas as well as xlrd.
import pandas as pd
import xlrd
import os
Path="C:downloads"
wb = pd.ExcelFile(Path+"\input.xlsx", engine=None)
sheet2 = pd.read_excel(wb, sheet_name="Sheet1")
Excel_Filter=sheet2[sheet2['Name']=='Test']
Excel_Filter.to_excel("C:downloads\output.xlsx", index=None)
wb2=xlrd.open_workbook(Path+"\output.xlsx")
df=wb2.sheet_by_name("Sheet1")
x=df.nrows
y=df.ncols
for i in range(0,x):
for j in range(0,y):
A=str(df.cell_value(i,j))
f=open(Path+"\emails.txt", "a")
f.write(A+"t")
f.close()
f=open(Path+"\emails.txt", "a")
f.write("n")
f.close()
os.remove(Path+"\output.xlsx")
print(Excel_Filter)
We need to first generate the xlsx file with filtered data and then convert the information into a text file.
Depending on requirements, we can use n t for loops and type of data we want in the text file.
If you have a Dataframe that is an output of pandas compare method, such a dataframe looks like below when it is printed:
grossRevenue netRevenue defaultCost
self other self other self other
2098 150.0 160.0 NaN NaN NaN NaN
2110 1400.0 400.0 NaN NaN NaN NaN
2127 NaN NaN NaN NaN 0.0 909.0
2137 NaN NaN 0.000000 8.900000e+01 NaN NaN
2150 NaN NaN 0.000000 8.888889e+07 NaN NaN
2162 NaN NaN 1815.000039 1.815000e+03 NaN NaN
I was looking to persist the whole dataframe into a text file as its visible above. Using pandas’s to_csv or numpy’s savetxt does not achieve this goal. I used plain old print to log the same into a text file:
with open('file1.txt', mode='w') as file_object:
print(data_frame, file=file_object)