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Data Structure

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Break - Continue

OOPS

Class - Introduction

Functions - Methods

Functions Introduction

Input and Print

Errors

Types of Errors

Regular Expressions

re - Introduction

Openpyxl

Interaction with Excel

Openpyxl - Charts

String Methods with Excel

Database Connections

Sqlite3 - Excel

Framework - Flask

Flask Introduction

Create Database

Webapp with Sqlite3

Pandas

Pandas_DataFrame_Introduction

Dataframe Dates

Dataframe Operations

Pandas - TextFiles

Web Scraping

matplotlib

File Handling

Data from Text file

Pandas - Text File

By using Pandas we can retrieve Data from Text File

Through PANDAS we can interact with TEXT files and retieve the data and store in TUPLE

Import Pandas as Pd

df = pd.read_csv('Sales.txt')

Create Data Frame:
Default seperator " , "

df = pd.read_csv('Sales.txt', sep = ',')

Considered delimiter as ','

df = pd.read_csv('Sales.txt', delimiter = ',')

We can use delimiter as key word instad of sep

Export the data from Text file to Excel workbook

Used pandas to extract the data from textfile
Used Openpyxl package to export the data into excel workbook
Select manually begining row and end row in combo box
Begining row number(min row) should be greater than max row(end row)
Openpyxl creates excel workbook itself., you need not refer any workbook
Click on below image to watch video

df = pd.read_csv('Sales.txt', header=None)

If TEXT file doesn't consits of Header, then Pandas by default assigns headers for dataframe

df = pd.read_csv('Sales.txt', index_col=0, parse_dates=True)

Create Dataframe by making date field as Index_column, then do the slicing

names=['First','Second','Third']
df = pd.read_csv('Sales.txt', names = names )

names List considered as Headers
'names' is a key word which is used to refer header list

names=['First','Second','Third']
df = pd.read_csv('Sales.txt', names = names, index_col = 'First' )

"index_col" denotes about row index
We can assign index column as per our requirement

print(list(open('Sales.txt')))

Save the text file in a list

df = pd.read_csv('Sales.txt', skiprows = [0,3])

Skip the rows of 0,3 from dataframe

df = pd.read_csv("Sales.txt", skip_blank_lines=True)

enables to skip the blank lines

df = pd.read_csv('Sales.txt', skipfooter = 5)

Skip 5 rows from bottom

df = pd.read_csv("Sales.txt", usecols = [0,1])

df = pd.read_csv("Sales.txt", usecols = ['Item','Price'])

Create dataframe based on required column names\numbers

df = pd.read_csv('Sales.txt', nrows = 5)

Print N rows from top

df = pd.read_csv("Sales.txt", index_col = [0,1])

Consider multiple columns as index columns

Select the chart type from combo box
Select the column header from list box
Click the button to display and save the chart

df = pd.read_csv('Sales.txt', chunksize = 2)

Reads the textfile into filesize 2

Handling with Missing values

df = pd.read_csv('Sales_Null_Values.txt')

Missing values marked as NaN

Data extraction through Slicing

Import the data from Text file by creating Dataframe
Export the Data_Frame and Slicing methods into Excel through PANDAS
Create Bar charts in excel workbook using VBA Macros
Fill Max value bar with Yellow for all the charts

Pandas - iloc

Enables the user to slice the data based on Index position

and Operator

or Operator

Group by

print(dir(df.groupby('Item')))

stack

df = pd.read_csv("Sales.txt", sep = ',')
s = df.stack()