Adding a multi index to columns
I am attempting to concatenate multiple files together and output to an excel file. My plan was to read the data into a dataframe, perform a few calculations, then write the data to an excel sheet. I would like to add a second label to my dataframe that indicates the file from which it came. I believe that multiindexing is the way to go but I am unsure of how to add.
example of current dataframe:
readout readout
0 1.098 4.514
1 3.185 2.124
2 0.938 0.369
3 5.283 7.840
example of intended dataframe:
file_1 file_2
readout readout
0 1.098 4.514
1 3.185 2.124
2 0.938 0.369
3 5.283 7.840
Here is the code I am currently using.
# import excel sheet into dataframe
well_reads = pd.read_excel('File.xls', header=0)
# pull positive control and negative control samples into new dataframe
positive_control = well_reads[well_reads['Well'].str.contains('01')]
negative_control = well_reads[well_reads['Well'].str.contains('12')]
# drop postive control and negative control rows from initial dataframe
positive_control_wells = well_reads[well_reads['Well'].str.contains('01')]
index = positive_control_wells.index
well_reads = well_reads.drop(well_reads.index[index])
well_reads = well_reads.reset_index(drop=True)
negative_control_wells = well_reads[well_reads['Well'].str.contains('12')]
index = negative_control_wells.index
well_reads = well_reads.drop(well_reads.index[index])
well_reads = well_reads.reset_index(drop=True)
# Create data frame just containing reads and well id
neutralization_data = well_reads[['CPS (CPS)', 'Well']]
# set index to well id
neutralization_data = neutralization_data.set_index(['Well'])
# identify the geometric mean of the plate
geomean = scipy.stats.gmean(well_reads['CPS (CPS)'])
# identify the IC50 of the plate
IC_50 = geomean/2
# identify the IC80 of the plate
IC_80 = geomean * 0.2
# create a pandas excel writer using xlsxwriter as the engine
writer = pd.ExcelWriter('neutralization data.xlsx', engine='xlsxwriter')
# convert the dataframe to an xlsxwriter excel object
neutralization_data.to_excel(writer, sheet_name='Neutralization Data', startrow=1)
# close the pandas excel writer and output the file
writer.save()
python pandas multi-index xlsxwriter
add a comment |
I am attempting to concatenate multiple files together and output to an excel file. My plan was to read the data into a dataframe, perform a few calculations, then write the data to an excel sheet. I would like to add a second label to my dataframe that indicates the file from which it came. I believe that multiindexing is the way to go but I am unsure of how to add.
example of current dataframe:
readout readout
0 1.098 4.514
1 3.185 2.124
2 0.938 0.369
3 5.283 7.840
example of intended dataframe:
file_1 file_2
readout readout
0 1.098 4.514
1 3.185 2.124
2 0.938 0.369
3 5.283 7.840
Here is the code I am currently using.
# import excel sheet into dataframe
well_reads = pd.read_excel('File.xls', header=0)
# pull positive control and negative control samples into new dataframe
positive_control = well_reads[well_reads['Well'].str.contains('01')]
negative_control = well_reads[well_reads['Well'].str.contains('12')]
# drop postive control and negative control rows from initial dataframe
positive_control_wells = well_reads[well_reads['Well'].str.contains('01')]
index = positive_control_wells.index
well_reads = well_reads.drop(well_reads.index[index])
well_reads = well_reads.reset_index(drop=True)
negative_control_wells = well_reads[well_reads['Well'].str.contains('12')]
index = negative_control_wells.index
well_reads = well_reads.drop(well_reads.index[index])
well_reads = well_reads.reset_index(drop=True)
# Create data frame just containing reads and well id
neutralization_data = well_reads[['CPS (CPS)', 'Well']]
# set index to well id
neutralization_data = neutralization_data.set_index(['Well'])
# identify the geometric mean of the plate
geomean = scipy.stats.gmean(well_reads['CPS (CPS)'])
# identify the IC50 of the plate
IC_50 = geomean/2
# identify the IC80 of the plate
IC_80 = geomean * 0.2
# create a pandas excel writer using xlsxwriter as the engine
writer = pd.ExcelWriter('neutralization data.xlsx', engine='xlsxwriter')
# convert the dataframe to an xlsxwriter excel object
neutralization_data.to_excel(writer, sheet_name='Neutralization Data', startrow=1)
# close the pandas excel writer and output the file
writer.save()
python pandas multi-index xlsxwriter
Hi Morgan, could you add the code you're currently using to write the file?
– Nathan
Nov 15 '18 at 20:22
The alternative was to save all the file names into a list and then reopen the excel file and write each name to the appropriate cell, but I was trying to get it all in one go.
– Morgan Gladden
Nov 15 '18 at 20:34
add a comment |
I am attempting to concatenate multiple files together and output to an excel file. My plan was to read the data into a dataframe, perform a few calculations, then write the data to an excel sheet. I would like to add a second label to my dataframe that indicates the file from which it came. I believe that multiindexing is the way to go but I am unsure of how to add.
example of current dataframe:
readout readout
0 1.098 4.514
1 3.185 2.124
2 0.938 0.369
3 5.283 7.840
example of intended dataframe:
file_1 file_2
readout readout
0 1.098 4.514
1 3.185 2.124
2 0.938 0.369
3 5.283 7.840
Here is the code I am currently using.
# import excel sheet into dataframe
well_reads = pd.read_excel('File.xls', header=0)
# pull positive control and negative control samples into new dataframe
positive_control = well_reads[well_reads['Well'].str.contains('01')]
negative_control = well_reads[well_reads['Well'].str.contains('12')]
# drop postive control and negative control rows from initial dataframe
positive_control_wells = well_reads[well_reads['Well'].str.contains('01')]
index = positive_control_wells.index
well_reads = well_reads.drop(well_reads.index[index])
well_reads = well_reads.reset_index(drop=True)
negative_control_wells = well_reads[well_reads['Well'].str.contains('12')]
index = negative_control_wells.index
well_reads = well_reads.drop(well_reads.index[index])
well_reads = well_reads.reset_index(drop=True)
# Create data frame just containing reads and well id
neutralization_data = well_reads[['CPS (CPS)', 'Well']]
# set index to well id
neutralization_data = neutralization_data.set_index(['Well'])
# identify the geometric mean of the plate
geomean = scipy.stats.gmean(well_reads['CPS (CPS)'])
# identify the IC50 of the plate
IC_50 = geomean/2
# identify the IC80 of the plate
IC_80 = geomean * 0.2
# create a pandas excel writer using xlsxwriter as the engine
writer = pd.ExcelWriter('neutralization data.xlsx', engine='xlsxwriter')
# convert the dataframe to an xlsxwriter excel object
neutralization_data.to_excel(writer, sheet_name='Neutralization Data', startrow=1)
# close the pandas excel writer and output the file
writer.save()
python pandas multi-index xlsxwriter
I am attempting to concatenate multiple files together and output to an excel file. My plan was to read the data into a dataframe, perform a few calculations, then write the data to an excel sheet. I would like to add a second label to my dataframe that indicates the file from which it came. I believe that multiindexing is the way to go but I am unsure of how to add.
example of current dataframe:
readout readout
0 1.098 4.514
1 3.185 2.124
2 0.938 0.369
3 5.283 7.840
example of intended dataframe:
file_1 file_2
readout readout
0 1.098 4.514
1 3.185 2.124
2 0.938 0.369
3 5.283 7.840
Here is the code I am currently using.
# import excel sheet into dataframe
well_reads = pd.read_excel('File.xls', header=0)
# pull positive control and negative control samples into new dataframe
positive_control = well_reads[well_reads['Well'].str.contains('01')]
negative_control = well_reads[well_reads['Well'].str.contains('12')]
# drop postive control and negative control rows from initial dataframe
positive_control_wells = well_reads[well_reads['Well'].str.contains('01')]
index = positive_control_wells.index
well_reads = well_reads.drop(well_reads.index[index])
well_reads = well_reads.reset_index(drop=True)
negative_control_wells = well_reads[well_reads['Well'].str.contains('12')]
index = negative_control_wells.index
well_reads = well_reads.drop(well_reads.index[index])
well_reads = well_reads.reset_index(drop=True)
# Create data frame just containing reads and well id
neutralization_data = well_reads[['CPS (CPS)', 'Well']]
# set index to well id
neutralization_data = neutralization_data.set_index(['Well'])
# identify the geometric mean of the plate
geomean = scipy.stats.gmean(well_reads['CPS (CPS)'])
# identify the IC50 of the plate
IC_50 = geomean/2
# identify the IC80 of the plate
IC_80 = geomean * 0.2
# create a pandas excel writer using xlsxwriter as the engine
writer = pd.ExcelWriter('neutralization data.xlsx', engine='xlsxwriter')
# convert the dataframe to an xlsxwriter excel object
neutralization_data.to_excel(writer, sheet_name='Neutralization Data', startrow=1)
# close the pandas excel writer and output the file
writer.save()
python pandas multi-index xlsxwriter
python pandas multi-index xlsxwriter
edited Nov 15 '18 at 20:32
Morgan Gladden
asked Nov 15 '18 at 20:20
Morgan GladdenMorgan Gladden
398
398
Hi Morgan, could you add the code you're currently using to write the file?
– Nathan
Nov 15 '18 at 20:22
The alternative was to save all the file names into a list and then reopen the excel file and write each name to the appropriate cell, but I was trying to get it all in one go.
– Morgan Gladden
Nov 15 '18 at 20:34
add a comment |
Hi Morgan, could you add the code you're currently using to write the file?
– Nathan
Nov 15 '18 at 20:22
The alternative was to save all the file names into a list and then reopen the excel file and write each name to the appropriate cell, but I was trying to get it all in one go.
– Morgan Gladden
Nov 15 '18 at 20:34
Hi Morgan, could you add the code you're currently using to write the file?
– Nathan
Nov 15 '18 at 20:22
Hi Morgan, could you add the code you're currently using to write the file?
– Nathan
Nov 15 '18 at 20:22
The alternative was to save all the file names into a list and then reopen the excel file and write each name to the appropriate cell, but I was trying to get it all in one go.
– Morgan Gladden
Nov 15 '18 at 20:34
The alternative was to save all the file names into a list and then reopen the excel file and write each name to the appropriate cell, but I was trying to get it all in one go.
– Morgan Gladden
Nov 15 '18 at 20:34
add a comment |
1 Answer
1
active
oldest
votes
Like you said, adding the multi-index columns will solve your issue before you write the output:
df=pd.DataFrame(0:[1.098,3.185,0.938, 5.283],1:[4.514,2.124,0.369, 7.840])
df.columns=pd.MultiIndex.from_tuples([('file1','readout'),('file2','readout')])
gives
file1 file2
readout readout
0 1.098 4.514
1 3.185 2.124
2 0.938 0.369
3 5.283 7.840
Thanks! Works perfectly.
– Morgan Gladden
Nov 15 '18 at 21:45
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Like you said, adding the multi-index columns will solve your issue before you write the output:
df=pd.DataFrame(0:[1.098,3.185,0.938, 5.283],1:[4.514,2.124,0.369, 7.840])
df.columns=pd.MultiIndex.from_tuples([('file1','readout'),('file2','readout')])
gives
file1 file2
readout readout
0 1.098 4.514
1 3.185 2.124
2 0.938 0.369
3 5.283 7.840
Thanks! Works perfectly.
– Morgan Gladden
Nov 15 '18 at 21:45
add a comment |
Like you said, adding the multi-index columns will solve your issue before you write the output:
df=pd.DataFrame(0:[1.098,3.185,0.938, 5.283],1:[4.514,2.124,0.369, 7.840])
df.columns=pd.MultiIndex.from_tuples([('file1','readout'),('file2','readout')])
gives
file1 file2
readout readout
0 1.098 4.514
1 3.185 2.124
2 0.938 0.369
3 5.283 7.840
Thanks! Works perfectly.
– Morgan Gladden
Nov 15 '18 at 21:45
add a comment |
Like you said, adding the multi-index columns will solve your issue before you write the output:
df=pd.DataFrame(0:[1.098,3.185,0.938, 5.283],1:[4.514,2.124,0.369, 7.840])
df.columns=pd.MultiIndex.from_tuples([('file1','readout'),('file2','readout')])
gives
file1 file2
readout readout
0 1.098 4.514
1 3.185 2.124
2 0.938 0.369
3 5.283 7.840
Like you said, adding the multi-index columns will solve your issue before you write the output:
df=pd.DataFrame(0:[1.098,3.185,0.938, 5.283],1:[4.514,2.124,0.369, 7.840])
df.columns=pd.MultiIndex.from_tuples([('file1','readout'),('file2','readout')])
gives
file1 file2
readout readout
0 1.098 4.514
1 3.185 2.124
2 0.938 0.369
3 5.283 7.840
answered Nov 15 '18 at 20:40
kevins_1kevins_1
3431213
3431213
Thanks! Works perfectly.
– Morgan Gladden
Nov 15 '18 at 21:45
add a comment |
Thanks! Works perfectly.
– Morgan Gladden
Nov 15 '18 at 21:45
Thanks! Works perfectly.
– Morgan Gladden
Nov 15 '18 at 21:45
Thanks! Works perfectly.
– Morgan Gladden
Nov 15 '18 at 21:45
add a comment |
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Hi Morgan, could you add the code you're currently using to write the file?
– Nathan
Nov 15 '18 at 20:22
The alternative was to save all the file names into a list and then reopen the excel file and write each name to the appropriate cell, but I was trying to get it all in one go.
– Morgan Gladden
Nov 15 '18 at 20:34