Dataframe statistical info

import pandas as pd# Create data frame from csv filedf=pd.read_csv(“e://data/state-population.csv”)#Statistical information about data framedf.describe() Output

Dataframe metadata

import pandas as pd # Create data frame from csv file df=pd.read_csv(“e://data/state-population.csv”) #Meta data about data frame df.info() Output <class ‘pandas.core.frame.DataFrame’> RangeIndex: 2544 entries, 0 to 2543 Data columns (total 4 columns): state/region 2544 non-null object ages 2544 non-null object year 2544 non-null int64 population 2524 non-null float64 dtypes: float64(1), int64(1), object(2) memory usage: 79.6+

Dataframe index

import pandas as pd# Create data frame from csv filedf=pd.read_csv(“e://data/state-population.csv”)print(df.set_index(‘year’).head(3)) Outputstate/region ages populationyear 2012 AL under18 1117489.02012 AL total 4817528.02010 AL under18 1130966.0 #indexing on multiple columnsprint(df.set_index([‘year’,’state/region’]).head(3)) Outputages populationyear state/region 2012 AL under18 1117489.0AL total 4817528.02010 AL under18 1130966.0

Dataframe rename columns

import pandas as pd# Create data frame from csv filedf=pd.read_csv(“e://data/state-population.csv”)# Rename column state/region to state-regionprint(df.rename(columns={‘state/region’:’state-region’}).head(2)) Outputstate-region ages year population0 AL under18 2012 1117489.01 AL total 2012 4817528.0 #Change column names in upper caseprint(df.rename(str.upper,axis=’columns’).head(2)) OutputSTATE/REGION AGES YEAR POPULATION0 AL under18 2012 1117489.01 AL total 2012 4817528.0

Dataframe logical operators

# Dataframe data retrieval using & logical operator.import pandas as pd# Create data frame from csv filedf=pd.read_csv(“e://data/state-population.csv”)# Data retrieval for state AL in year 2012print(df[(df[‘state/region’]==’AL’) & (df[‘year’]==2012)].head()) Output state/region ages year population0 AL under18 2012 1117489.01 AL total 2012 4817528.0 # data retrieval state AL and NY (First 5 rows)df[(df[‘state/region’]==’AL’) | (df[‘state/region’]==’NY’)].head() Output

Dataframe condition

import pandas as pd # Create data frame from csv file df=pd.read_csv(“e://data/state-population.csv”) # data retrieval for year 2013 (First 5 rows) print(df[df.year==2013].head()) Output      state/region ages year population 8    AL under18 2013 1111481.0 9    AL total 2013 4833722.0 86   AK under18 2013 188132.0 87   AK total 2013 735132.0 102 AZ under18 2013

Data frame column names

import pandas as pd # Create data frame from csv file df=pd.read_csv(“e://data/state-population.csv”) # Data frame column names df.columns Output Index([‘state/region’, ‘ages’, ‘year’, ‘population’], dtype=’object’)

Dataframe head, tail and shape

import pandas as pd # Create data frame from csv file df=pd.read_csv(“e://data/state-population.csv”) # First 3 rows retrieval print(df.head(3)) Output state/region ages year population 0 AL under18 2012 1117489.0 1 AL total 2012 4817528.0 2 AL under18 2010 1130966.0 import pandas as pd df=pd.read_csv(“e://data/state-population.csv”) # last 3 rows retrieval print(df.tail(3)) Output state/region ages year population 2541

Remove $ symbol from dataframe column

import pandas as pddfEmp = pd.read_csv(“e:\\data\\emp_details.csv”)dfEmp Output import pandas as pddfEmp = pd.read_csv(“e:\\data\\emp_details.csv”)# Remove $ sign from Salary columndfEmp[‘Salary’]=dfEmp[‘Salary’].str.replace(‘$’,”)dfEmp Output

Tk – Window Demo

from tkinter import * # Create window root=Tk() # Window title root.title(“Window Demo”) # Adjust window size according widget root.propagate(1) # Window background color root.configure(background=’silver’) ”’ fg – foreground color – text color bg background color ”’ # Crete Label objects lblgm=Label(root,text=”Good Evening”,fg=’green’,bg=’silver’,font=”Arial 25 bold”) lbleve=Label(root,text=”Good Morning”,fg=’red’,bg=’silver’,font=”Tahoma 15 bold”) # Include label objects in window