Append values to Dataframe in loop and if conditions - python-3.x

Need help please.
I have a dataframe that reads rows from Excel and appends to Dataframe if certain columns exist.
I need to add an additional Dataframe if the columns don't exist in a sheet and append filename and sheetname and write all the file names and sheet names for those sheets to an excel file. Also I want the values to be unique.
I tried adding to dfErrorList but it only showed the last sheetname and filename and repeated itself many times in the output excel file
from xlsxwriter import Workbook
import pandas as pd
import openpyxl
import glob
import os
path = 'filestoimport/*.xlsx'
list_of_dfs = []
list_of_dferror = []
dfErrorList = pd.DataFrame() #create empty df
for filepath in glob.glob(path):
xl = pd.ExcelFile(filepath)
# Define an empty list to store individual DataFrames
for sheet_name in xl.sheet_names:
df = pd.read_excel(filepath, sheet_name=sheet_name)
df['sheetname'] = sheet_name
file_name = os.path.basename(filepath)
df['sourcefilename'] = file_name
if "Project ID" in df.columns and "Status" in df.columns:
print('')
*else:
dfErrorList['sheetname'] = df['sheetname'] # adds `sheet_name` into the column
dfErrorList['sourcefilename'] = df['sourcefilename']
continue
list_of_dferror.append((dfErrorList))
df['Status'].fillna('', inplace=True)
df['Added by'].fillna('', inplace=True)
list_of_dfs.append(df)
# # Combine all DataFrames into one
data = pd.concat(list_of_dfs, ignore_index=True)
dataErrors = pd.concat(list_of_dferror, ignore_index=True)
dataErrors.to_excel(r'error.xlsx', index=False)
# data.to_excel("total_countries.xlsx", index=None)

Related

In python, how to concatenate corresponding sheets in multiple excel files

How do I concatenate multiple xlsx files with the same sheet_names. For example,
I have 3 xlsx files, Rob_schedule.xlsx, Mike_schdule.xlsx and Jerome_schedule.xlsx.
Each file has the following sheet/tab names : home, office & school.
The code below generates the 3 xlsx files ( you can copy + paste and run to generate the excel files)
##############################Generating the data for Rob_schedule.xlsx########################
import pandas as pd
import numpy as np
df= {
'Date':[10232020,10242020,10252020,10262020],
'Class':['AP_Bio','AP_Chem','Physics','History'],
'Period':[3,1,2,4]}
school = pd.DataFrame(df,columns = ['Date','Class','Period'])
school
df2= {
'Date':[10232020,10242020,10252020,10262020],
'Meeting':['MQ1','MQ6','MQ2','MQ8'],
'Lunch':[1,1,1,3],
'code':['java','python','C','C++']}
office = pd.DataFrame(df2,columns = ['Date','Meeting','Lunch','code'])
office
df3= {
'cooking':['C','B','D','B'],
'Laundry':['color','white','White','color'],
'cleaning':['balcony','garage','restroom','bathroom']}
home = pd.DataFrame(df3,columns = ['cooking','Laundry','cleaning'])
home
import pandas as pd
#initialze the excel writer
writer = pd.ExcelWriter('Rob_schedule.xlsx', engine='xlsxwriter')
#store your dataframes in a dict, where the key is the sheet name you want
frames = {'home':home, 'office':office,
'school':school}
#now loop thru and put each on a specific sheet
for sheet, frame in frames.items():
frame.to_excel(writer, sheet_name = sheet,index = False)
#critical last step
writer.save()
################################ generating Mike_schedule.xlsx###################################
import pandas as pd
import numpy as np
df= {
'Date':[10232020,10242020,10252020,10262020],
'Class':['AP_Bio','AP_Chem','Physics','History'],
'Period':[3,1,2,4]}
school = pd.DataFrame(df,columns = ['Date','Class','Period'])
school
df2= {
'Date':[10232020,10242020,10252020,10262020],
'Meeting':['MQ1','MQ2','MQ4','MQ5'],
'Lunch':[1,1,1,3],
'code':['javascript','R','C','C++']}
office = pd.DataFrame(df2,columns = ['Date','Meeting','Lunch','code'])
office
df3= {
'cooking':['A','B','D','B'],
'Laundry':['color','white','white','color'],
'cleaning':['patio','garage','living_room','bathroom']}
home = pd.DataFrame(df3,columns = ['cooking','Laundry','cleaning'])
home
#initialze the excel writer
writer = pd.ExcelWriter('Mike_schedule.xlsx', engine='xlsxwriter')
#store your dataframes in a dict, where the key is the sheet name you want
frames = {'home':home, 'office':office,
'school':school}
#now loop thru and put each on a specific sheet
for sheet, frame in frames.items(): # .use .items for python 3.X
frame.to_excel(writer, sheet_name = sheet,index = False)
#critical last step
writer.save()
######################### Generate Jerome schedule###########################################
df= {
'Date':[10232020,10242020,10252020,10262020],
'Class':['French','Math','Physics','History'],
'Period':[3,1,2,4]}
school = pd.DataFrame(df,columns = ['Date','Class','Period'])
school
df2= {
'Date':[10232020,10242020,10252020,10262020],
'Meeting':['MQ1','MQ2','MQ4','MQ5'],
'Lunch':[1,1,1,3],
'code':['javascript','python','R','C++']}
office = pd.DataFrame(df2,columns = ['Date','Meeting','Lunch','code'])
office
df3= {
'cooking':['X','B','D','C'],
'Laundry':['color','white','white','color'],
'cleaning':['patio','garage','living_room','bathroom']}
home = pd.DataFrame(df3,columns = ['cooking','Laundry','cleaning'])
home
import pandas as pd
#initialze the excel writer
writer = pd.ExcelWriter('Jerome_schedule.xlsx', engine='xlsxwriter')
#store your dataframes in a dict, where the key is the sheet name you want
frames = {'home':home, 'office':office,
'school':school}
#now loop thru and put each on a specific sheet
for sheet, frame in frames.items(): # .use .items for python 3.X
frame.to_excel(writer, sheet_name = sheet,index = False)
#critical last step
writer.save()
I want to
concatenate the corresponding sheets/tabs :home, office, and school for Rob_schedule.xlsx,Mike_schedule.xlsx & Jerome_schedule.xlsx
export the concatenated dataframes as family_schedule.xlsx with home, office and school tabs
My attempt:
# This code concatenates all the tabs into one tab, but what I want is to concatenate all by their corresponding sheet/tab names
import pandas as pd
path = os.chdir(r'mypath\\')
files = os.listdir(path)
files
# pull files with `.xlsx` extension
excel_files = [file for file in files if '.xlsx' in file]
excel_files
def create_df_from_excel(file_name):
file = pd.ExcelFile(file_name)
names = file.sheet_names
return pd.concat([file.parse(name) for name in names])
df = pd.concat(
[create_df_from_excel(xl) for xl in excel_files]
)
# save the data frame
writer = pd.ExcelWriter('family_reschedule.xlsx')
df.to_excel(writer, '')
writer.save()

I would iterate over each file, and then over each worksheet, adding each sheet to a different list based on the sheet name.
Then you'll have a structure like...
{
'sheet1': [df_file1_sheet1, df_file2_sheet1, df_file3_sheet1],
'sheet2': [df_file1_sheet2, df_file2_sheet2, df_file3_sheet2],
'sheet3': [df_file1_sheet3, df_file2_sheet3, df_file3_sheet3],
}
Then concatenate each list in to a single dataframe, them write the three dataframes to an excel file.
# This part is just your own code, I've added it here because you
# couldn't figure out where `excel_files` came from
#################################################################
import os
import pandas as pd
path = os.chdir(r'mypath\\')
files = os.listdir(path)
files
# pull files with `.xlsx` extension
excel_files = [file for file in files if '.xlsx' in file]
excel_files
# This part is my actual answer
###############################
from collections import defaultdict
worksheet_lists = defaultdict(list)
for file_name in excel_files:
workbook = pd.ExcelFile(file_name)
for sheet_name in workbook.sheet_names:
worksheet = workbook.parse(sheet_name)
worksheet['source'] = file_name
worksheet_lists[sheet_name].append(worksheet)
worksheets = {
sheet_name: pd.concat(sheet_list)
for (sheet_name, sheet_list)
in worksheet_lists.items()
}
writer = pd.ExcelWriter('family_reschedule.xlsx')
for sheet_name, df in worksheets.items():
df.to_excel(writer, sheet_name=sheet_name, index=False)
writer.save()

Consider building a list of concatenated data frames with list/dict comprehensions by running an outer iteration across sheet names and inner iteration across workbooks:
import pandas as pd
path = "/path/to/workbooks"
workbooks = [f for f in os.listdir(path) if f.endswith(".xlsx")]
sheets = ["home", "office", "school"]
df_dicts = {
sh: pd.concat(
[pd.read_excel(os.path.join(path, wb), sheet_name=sh)
for wb in workbooks]
)
for sh in sheets
}
Then, export to single file:
with pd.ExcelWriter('family_reschedule.xlsx') as writer:
for sh, df in df_dict.items():
df.to_excel(writer, sheet_name=sh, index=False)
writer.save()

Can we copy one column from excel and convert it to a list in Python?

I use
df = pd.read_clipboard()
list_name = df['column_name'].to_list()
but this is a bit long method for me. I want to copy a column and convert in python and then apply some function so that the copied text is converted to a list.

this will read a excel column as list
import xlrd
book = xlrd.open_workbook('Myfile.xlsx') #path to your file
sheet = book.sheet_by_name("Sheet1") #Sheet name
def Readlist(Element, Column):
for _ in range(1,sheet.nrows):
Element.append(str(sheet.row_values(_)[Column]))
pass
column1 = [] # List name
Readlist(column1, 1) # Column Number is 1 here
pirnt(column1)
Read a specified column as list use Readlist function, intialize [] variable before using that.
Using Pandas:
import pandas as pd
df = pd.read_excel("path.xlsx", index_col=None, na_values=['NA'], usecols = "A")
mylist = list(df[0])
print(mylist)

Storing outputdata in CSV using python

I have extracted data from different excel sheets spread in different folders, I have organized the folders numerically from 2015 to 2019 and each folder has twelve subfolders (from 1 to 12) here's my code:
import os
from os import walk
import pandas as pd
path = r'C:\Users\Sarah\Desktop\IOMTest'
my_files = []
for (dirpath, dirnames, filenames) in walk(path):
my_files.extend([os.path.join(dirpath, fname) for fname in filenames])
all_sheets = []
for file_name in my_files:
#Display sheets names using pandas
pd.set_option('display.width',300)
mosul_file = file_name
xl = pd.ExcelFile(mosul_file)
mosul_df = xl.parse(0, header=[1], index_col=[0,1,2])
#Read Excel and Select columns
mosul_file = pd.read_excel(file_name, sheet_name = 0 ,
index_clo=None, na_values= ['NA'], usecols = "A, E, G, H , L , M" )
#Remove NaN values
data_mosul_df = mosul_file.apply (pd.to_numeric, errors='coerce')
data_mosul_df = mosul_file.dropna()
print(data_mosul_df)
then I saved the extracted columns in a csv file
def save_frames(frames, output_path):
for frame in frames:
frame.to_csv(output_path, mode='a+', header=False)
if __name__ == '__main__':
frames =[pd.DataFrame(data_mosul_df)]
save_frames(frames, r'C:\Users\Sarah\Desktop\tt\c.csv')
My problem is that when I open the csv file it seems that it doesn't store all the data but only the last excel sheet that it has read or sometimes the two last excel sheets. however, when I print my data inside the console (in Spyder) I see that all the data are treated
data_mosul_df = mosul_file.apply (pd.to_numeric, errors='coerce')
data_mosul_df = mosul_file.dropna()
print(data_mosul_df)
the picture below shows the output csv created. I am wondering if it is because from Column A to Column E the information are the same ? so that's why it overwrite ?
I would like to know how to modify the code so that it extract and store the data chronologically from folders (2015 to 2019) taking into accout subfolders (from 1 to 12) in each folder and how to create a csv that stores all the data ? thank you

Rewrite your loop:
for file_name in my_files:
#Display sheets names using pandas
pd.set_option('display.width',300)
mosul_file = file_name
xl = pd.ExcelFile(mosul_file)
mosul_df = xl.parse(0, header=[1], index_col=[0,1,2])
#Read Excel and Select columns
mosul_file = pd.read_excel(file_name, sheet_name = 0 ,
index_clo=None, na_values= ['NA'], usecols = "A, E, G, H , L , M" )
#Remove NaN values
data_mosul_df = mosul_file.apply (pd.to_numeric, errors='coerce')
data_mosul_df = mosul_file.dropna()
#Make a list of df's
all_sheets.append(data_mosul_df)
Rewrite your save_frames:
def save_frames(frames, output_path):
frames.to_csv(output_path, mode='a+', header=False)
Rewrite your main:
if __name__ == '__main__':
frames = pd.concat(all_sheets)
save_frames(frames, r'C:\Users\Sarah\Desktop\tt\c.csv')

How to read an excel file with multiple sheets using for loop in python

This is what i try
from pathlib import Path
loc = Path('D:\DataSciSpec\Practice\Forloopindict.xlsx')
dict = pd.read_excel(loc,sheetname = None)
for i in dict.keys():
print(i)
I get the name of sheets
Sheet4
Sheet3
Sheet2
Sheet1
I can also see the sheet content one by one
for i in dict.keys():
print(dict[i].head())
But how put this data in n data frames (equal to no of sheets)
and then append one to another

This will create a single dataframe (df_full) with the data from all sheets.
import pandas as pd
loc = r'D:\DataSciSpec\Practice\Forloopindict.xlsx'
workbook = pd.read_excel(loc,sheet_name = None)
df_full = pd.DataFrame()
for _, sheet in workbook.items():
df_full = df_full.append(sheet)
# Reset index or you'll have duplicates
df_full = df_full.reset_index(drop=True)

Appending Columns from several worksheets Python

I am trying to import certain columns of data from several different sheets inside of a workbook. However, while appending it only seems to append 'q2 survey' to a new workbook. How do I get this to append properly?
import sys, os
import pandas as pd
import xlrd
import xlwt
b = ['q1 survey', 'q2 survey','q3 survey'] #Sheet Names
df_t = pd.DataFrame(columns=["Month","Date", "Year"]) #column Name
xls = "path_to_file/R.xls"
sheet=[]
df_b=pd.DataFrame()
pd.read_excel(xls,sheet)
for sheet in b:
df=pd.read_excel(xls,sheet)
df.rename(columns=lambda x: x.strip().upper(), inplace=True)
bill=df_b.append(df[df_t])
bill.to_excel('Survey.xlsx', index=False)

I think if you do:
b = ['q1 survey', 'q2 survey','q3 survey'] #Sheet Names
list_col = ["Month","Date", "Year"] #column Name
xls = "path_to_file/R.xls"
#create the empty df named bill to append after
bill= pd.DataFrame(columns = list_col)
for sheet in b:
# read the sheet
df=pd.read_excel(xls,sheet)
df.rename(columns=lambda x: x.strip().upper(), inplace=True)
# need to assign bill again
bill=bill.append(df[list_col])
# to excel
bill.to_excel('Survey.xlsx', index=False)
it should work and correct the errors in your code, but you can do a bit differently using pd.concat:
list_sheet = ['q1 survey', 'q2 survey','q3 survey'] #Sheet Names
list_col = ["Month","Date", "Year"] #column Name
# read once the xls file and then access the sheet in the loop, should be faster
xls_file = pd.ExcelFile("path_to_file/R.xls")
#create a list to append the df
list_df_to_concat = []
for sheet in list_sheet :
# read the sheet
df= pd.read_excel(xls_file, sheet)
df.rename(columns=lambda x: x.strip().upper(), inplace=True)
# append the df to the list
list_df_to_concat.append(df[list_col])
# to excel
pd.concat(list_df_to_concat).to_excel('Survey.xlsx', index=False)

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