!pip install cleantext

#Importing the clean text library
from cleantext import clean# Sample texttext = """ Zürich, largest city of Switzerland and capital of the canton of 633Zürich. Located in an Alu017eupine. (https://google.com). Currency is not ₹"""# Cleaning the "text" with clean textclean(text, fix_unicode=True, to_ascii=True, lower=True, no_urls=True, no_numbers=True, no_digits=True, no_currency_symbols=True, no_punct=True, replace_with_punct=" ", replace_with_url="", replace_with_number="", replace_with_digit=" ", replace_with_currency_symbol="Rupees")

!pip install drawdata

# Importing the drawdata from drawdata import draw_scatterdraw_scatter()

#Reading the clipboardimport pandas as pd df = pd.read_clipboard(sep=",")
df

!pip install autoviz

# Importing Autoviz class from the autoviz library
from autoviz.AutoViz_Class import AutoViz_Class#Initialize the Autoviz class in a object called df
df = AutoViz_Class()# Using Iris Dataset and passing to the default parametersfilename = "Iris.csv"
sep = ","graph = df.AutoViz( filename, sep=",", depVar="", dfte=None, header=0, verbose=0, lowess=False, chart_format="svg", max_rows_analyzed=150000, max_cols_analyzed=30,
)

#First install mitoinstaller in the command prompt
pip install mitoinstaller# Then, run the installer in the command prompt
python -m mitoinstaller install# Then, launch Jupyter lab or jupyter notebook from the command prompt
python -m jupyter lab

# Importing mitosheet and ruuning this in Jupyter labimport mitosheet
mitosheet.sheet()

from mitosheet import * # Import necessary functions from Mito
register_analysis('UUID-119387c0-fc9b-4b04-9053-802c0d428285') # Let Mito know which analysis is being run# Imported C:UsersDhilipDownloadsarchive (29)Iris.csv
import pandas as pd
Iris_csv = pd.read_csv('C:UsersDhilipDownloadsarchive (29)Iris.csv')# Added column G to Iris_csv
Iris_csv.insert(6, 'G', 0)# Set G in Iris_csv to =AVG(SepalLengthCm)
Iris_csv['G'] = AVG(Iris_csv['SepalLengthCm'])# Renamed G to Avg_Sepal in Iris_csv
Iris_csv.rename(columns={"G": "Avg_Sepal"}, inplace=True)

!pip3 install -U git+https://github.com/PrithivirajDamodaran/Gramformer.git

gf = Gramformer(models = 1, use_gpu = False) # 1=corrector, 2=detector (presently model 1 is working, 2 has not implemented)

#Giving sample text for correction under gf.correctgf.correct(""" New Zealand is island countrys in southwestern Paciific Ocaen. Country population was 5 million """)

!pip install git+https://github.com/PrithivirajDamodaran/Styleformer.git

sf = Styleformer(style = 0)# style = [0=Casual to Formal, 1=Formal to Casual, 2=Active to Passive, 3=Passive to Active etc..]

# Converting casual to formal sf.transfer("I gotta go")

# Formal to casual sf = Styleformer(style = 1) # 1 -> Formal to casual# Converting formal to casual
sf.transfer("Please leave this place")

# Active to Passive sf = Styleformer(style = 2) # 2-> Active to Passive# Converting active to passive
sf.transfer("We are going to watch a movie tonight.")

# passive to active
sf = Styleformer(style = 2) # 2-> Active to Passive# Converting passive to active
sf.transfer("Tenants are protected by leases")