Selecting multiple excel files in different folders and comparing them with pandas - pandas

I am trying to compare multiple excel files with pandas and glob, but i am getting the folowing output from my code:
Empty DataFrame
Columns: []
Index: []
This is my code:
import glob
import pandas as pd
all_data = pd.DataFrame()
path = 'S:\data\**\C_76_00_a?.xlsx'
for f in glob.glob(path):
df = pd.read_excel(f, sheet_name=None, ignore_index=True, skiprows=10, usecols=4)
cdf = pd.concat(df.values(), axis=1)
all_data = all_data.append(cdf,ignore_index=True)
print(all_data)
I am using jupyter notebook and the folder structure is \2021\12\2021-30-12\ and the file name containg "C_76_00_a" +"something unknown +".xlsx"
Below is a screenshot of the type of document i am trying to compare

Related

assigning csv file to a variable name

I have a .csv file, i uses pandas to read the .csv file.
import pandas as pd
from pandas import read_csv
data=read_csv('input.csv')
print(data)
0 1 2 3 4 5
0 -3.288733e-08 2.905263e-08 2.297046e-08 2.052534e-08 3.767194e-08 4.822049e-08
1 2.345769e-07 9.462636e-08 4.331173e-08 3.137627e-08 4.680112e-08 6.067109e-08
2 -1.386798e-07 1.637338e-08 4.077676e-08 3.339685e-08 5.020153e-08 5.871679e-08
3 -4.234607e-08 3.555008e-08 2.563824e-08 2.320405e-08 4.008257e-08 3.901410e-08
4 3.899913e-08 5.368551e-08 3.713510e-08 2.367323e-08 3.172775e-08 4.799337e-08
My aim is to assign the file to a column name so that i can access the data in later time. For example by doing something like
new_data= df['filename']
filename
0 -3.288733e-08,2.905263e-08,2.297046e-08,2.052534e-08,3.767194e-08,4.822049e-08
1 2.345769e-07,9.462636e-08,4.331173e-08,3.137627e-08,4.680112e-08, 6.067109e-08
2 -1.386798e-07,1.637338e-08,4.077676e-08,3.339685e-08,5.020153e-08,5.871679e-08
3 -4.234607e-08,3.555008e-08,2.563824e-08,2.320405e-08,4.008257e-08,3.901410e-08
4 3.899913e-08,5.368551e-08,3.713510e-08,2.367323e-08,3.172775e-08,4.799337e-08

I don't really like it (and I still don't completely get the point), but you could just read in your data as 1 column (by using a 'wrong' seperator) and renaming the column.
import pandas as pd
filename = 'input.csv'
df = pd.read_csv(filename, sep=';')
df.columns = [filename]
If you then wish, you could add other files by doing the same thing (with a different name for df at first) and then concatenate that with df.
A more usefull approach IMHO would be to add the dataframe to a dictionary (or a list would be possible).
import pandas as pd
filename = 'input.csv'
df = pd.read_csv(filename)
data_dict = {filename: df}
# ... Add multiple files to data_dict by repeating steps above in a loop
You can then access your data later on by calling data_dict[filename] or data_dict['input.csv']

adding source.csv file name to first column while merging multiple csv

I have thousands of CSv files, which i have to merge before analysis. I want to add columns having source file name with each row having a sub-number (example SRR1313_1, SRR1313_2, SRR1313_3 and so on.
One limitaion of merging huge data is RAM, which results in error. here is the cmds i tried, but cant add sub-numbering after file name
import pandas as pd
import glob
import os
from pathlib import Path
path = '/home/shoeb/Desktop/vergDB/DATA'
folder = glob.glob(os.path.join("*.csv"))
out_file = "final_big_file.tsv"
first_file=True
for file in folder:
df = pd.read_csv(file, skiprows=1)
df = df.dropna()
df.insert(loc=0, column ="sequence_id", value =file [0:10])
if first_file:
df.to_csv(out_file, sep= "\t", index=False)
first_file=False
else:
df.to_csv(out_file, sep= "\t", index=False, header= False, mode='a')

How can I sort and replace the rank of each item instead of it's value before sorting in a merged final csv?

I have 30 different csv files and each row begin with date and some similar features measured for each of 30 items daily. The value of each feature is not important, but the rank they gain after sorting in each day is important. How can I have one merged csv from 30 separate csv with the rank of each feature?

If your files are all the same format, you can do a loop and consolidate in a single data frame. From there you can manipulate as needed:
import pandas as pd
import glob
path = r'C:\path_to_files\files' # use your path
all_files = glob.glob(path + "/*.csv")
li = []
for filename in all_files:
df = pd.read_csv(filename, index_col=None, header=0)
li.append(df)
frame = pd.concat(li, axis=0, ignore_index=True)
Other examples of the same thing: Import multiple csv files into pandas and concatenate into one DataFrame.

How to subset a dataframe, groupby and export the dataframes as multiple sheets of a one excel file in Python

Python newbie here
In the dataset below:
import pandas as pd
import numpy as np
data = {'Gender':['M','M','M','M','F','F','F','F','M','M','M','M','F','F','F','F'],
'Location':['NE','NE','NE','NE','SW','SW','SW','SW','SE','SE','SE','SE','NC','NC','NC','NC'],
'Type':['L','L','L','L','L','L','L','L','R','R','R','R','R','R','R','R'],
'PDP':['<10','<10','<10','<10',10,10,10,10,20,20,20,20,'>20','>20','>20','>20'],
'PDP_code':[1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4],
'diff':[-1,-1,-1,-1,0,0,0,0,1,1,1,1,3,3,3,3],
'series':[1,2,4,8,1,2,4,8,1,2,4,8,1,2,4,8],
'Revenue_YR1':[1150.78,1162.34,1188.53,1197.69,2108.07,2117.76,2129.48,1319.51,1416.87,1812.54,1819.57,1991.97,2219.28,2414.73,2169.91,2149.19],
'Revenue_YR2':[250.78,262.34,288.53,297.69,308.07,317.7,329.81,339.15,346.87,382.54,369.59,399.97,329.28,347.73,369.91,349.12],
'Revenue_YR3':[240.18,232.14,258.53,276.69,338.07,307.74,359.16,339.25,365.87,392.48,399.97,410.75,429.08,448.39,465.15,469.33],
'Revenue_YR4':[270.84,282.14,298.53,306.69,318.73,327.47,369.63,389.59,398.75,432.18,449.78,473.55,494.85,509.39,515.52,539.23],
'Revenue_YR5':[251.78,221.34,282.53,272.69,310.07,317.7,329.81,333.15,334.87,332.54,336.59,339.97,329.28,334.73,336.91,334.12],
'Revenue_YR6':[240.18,232.14,258.53,276.69,338.07,307.74,359.16,339.25,365.87,392.48,399.97,410.75,429.08,448.39,465.15,469.33],
'Revenue_YR7':[27.84,28.14,29.53,30.69,18.73,27.47,36.63,38.59,38.75,24.18,24.78,21.55,13.85,9.39,15.52,39.23],
'Revenue_YR8':[279.84,289.14,299.53,309.69,318.73,327.47,336.63,398.59,398.75,324.18,324.78,321.55,333.85,339.39,315.52,319.23],
}
df = pd.DataFrame(data,columns = ['Gender','Location','Type','PDP','PDP_code','diff','series',
'Revenue_YR1','Revenue_YR2','Revenue_YR3','Revenue_YR4','Revenue_YR5','Revenue_YR6',
'Revenue_YR7','Revenue_YR8'])
df.head(5)
I want a pythonic way of doing the following :
subset df into 4 dataframes / lists based on unique Location resulting in NE,SW,SE & NC dataframes
aggregating all the Revenue_YR columns while GroupBy series and PDP_code columns and export all the aggregated dataframes (NE,SW,SE & NC) as multiple sheets of one xlsx file
My attempt
### this code returns output of 1 df instead of 4 dfs, I need help aggregating each of the 4 dataframes and export them to 4 sheets of 12312021_output.xlsx
for i, part_df in df.groupby('Location'):
part_df.groupby(['series','PDP_code'])[['Revenue_YR1', 'Revenue_YR2','Revenue_YR3',
'Revenue_YR4', 'Revenue_YR5', 'Revenue_YR6', 'Revenue_YR7']].mean().unstack().style.background_gradient(cmap='Blues').to_excel('12312021_output.xlsx')
Please share your code.

You can use pandas.ExcelWriter, and your loop (which I improved slightly for readability):
import pandas as pd
with pd.ExcelWriter("output.xlsx") as writer:
cols = df.filter(like='Revenue_YR').columns
for g, d in df.groupby('Location'):
(d.groupby(['series','PDP_code'])[cols].mean().unstack()
.style.background_gradient(cmap='Blues')
).to_excel(writer, sheet_name=g)

Merge multiple csv files in python

Need help with merging multiple csv file
import pandas as pd
import glob
import csv
r1=glob.glob("path/*.csv")
wr1 = csv.writer(open("path/merge.csv",'wb'),delimiter = ',')
for files in r1:
rd=csv.reader(open(files,'r'), delimiter=',')
for row in rd:
print(row)
wr1.writerow(row)
I am getting a type error
TypeError: a bytes-like object is required, not 'str' not sure how to resolve this

Using pandas you can do it like this:
dfs = glob.glob('path/*.csv')
result = pd.concat([pd.read_csv(df) for df in dfs], ignore_index=True)
result.to_csv('path/merge.csv', ignore_index=True)