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import pandas as pd
import glob
csv_files = glob.glob("/*.CSV") # get list of files in directory
for csv_file in csv_files: # loop over all files in directory
data = pd.read_csv(csv_file) # load csv file
data = data[data["Fee"] != 0] # remove all rows where fee=0 (aka non purchases)
data = data[data["Gross"] > 0] # remove all rows where income < 0 (i.e. -ve)
data = data[['Subject', 'Gross']] # choose only subject and gross income columns
data['Subject']= data['Subject'].replace({'Old name': 'New name'}) # Manually rename duplicates with different names
data = data.groupby('Subject')['Gross'].agg(['sum','count']) # group by subject and aggregate the rows
data.index.names = ['Item Name'] # rename index to 'Item Name'
# get month from filename and rename columns
month = csv_file.split(".")[0].split("/")[-1]
month_count = month + ' Quantity Sold'
month_value = month + ' Sales in £'
data.rename(columns={'sum': month_value, 'count': month_count}, inplace=True)
# merge the each months into one dataframe
if csv_file == csv_files[0]:
fulldf = data
fulldf = fulldf.merge(data, on='Item Name', how='outer')
fulldf.fillna(0, inplace=True) # replace all NaN with 0
fulldf.drop(['Personal Item 1','Personal Item 2']) # remove personal sales with a specific name
quantity_cols = [col for col in fulldf.columns if 'Quantity' in col] # Choose quantity col labels
fulldf[quantity_cols] = fulldf[quantity_cols].astype(int) # convert quantity cols to type(int)
quantity_df = fulldf[quantity_cols] # select quantity cols only
quantity_df['Mean Quantity'] = quantity_df.mean(axis=1) # get mean over the time period
quantity_df.loc['Total']= quantity_df.sum() # get total of all rows
quantity_df.to_csv(r'/eBay Report by Quantity.csv') # Export dataframe back to CSV
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