Basic Data Types

Constant Floating-point value

Value	Python Expression
Hexa decimal a1	0xa1
	3.2e-12
Horizontal Tab character Newline (ASCII Linefeed) character The character with hexadecimal value `a0`	`'\t\n\xa0'` `"\t\n\xa0"` `'''\t\n\xa0'''` `"""\t\n\xa0"""`

Basic Operators

Python Arithmetic Operators

Operator	Description	Example
+ Addition	Adds values on either side of the operator.	10 + 20 = 30
- Subtraction	Subtracts right hand operand from left hand operand.	10 – 20 = -10
* Multiplication	Multiplies values on either side of the operator	10 * 20 = 200
/ Division	Divides left hand operand by right hand operand	20 / 10 = 2
% Modulus	Divides left hand operand by right hand operand and returns remainder	20 % 10 = 0
** Exponent	Performs exponential (power) calculation on operators	10**20 =10 to the power 20
//	Floor Division - The division of operands where the result is the quotient in which the digits after the decimal point are removed. But if one of the operands is negative, the result is floored, i.e., rounded away from zero (towards negative infinity) −	9//2 = 4 and 9.0//2.0 = 4.0 -11//3 = -4 -11.0//3 = -4.0

Python Comparison Operators

Below example is based on the condition as a=10, b=20

Operator	Description	Example
==	If the values of two operands are equal, then the condition becomes true.	(a == b) is not true.
!=	If values of two operands are not equal, then condition becomes true.	(a != b) is true.
<>	If values of two operands are not equal, then condition becomes true.	(a <> b) is true. This is similar to != operator.
>	If the value of left operand is greater than the value of right operand, then condition becomes true.	(a > b) is not true.
<	If the value of left operand is less than the value of right operand, then condition becomes true.	(a < b) is true.
>=	If the value of left operand is greater than or equal to the value of right operand, then condition becomes true.	(a >= b) is not true.
<=	If the value of left operand is less than or equal to the value of right operand, then condition becomes true.	(a <= b) is true.

Data Structures - List / Set / Tuple / Dictionary

List

list1 = ['physics', 'chemistry', 1997, 2000];
list2 = [1, 2, 3, 4, 5 ];
list3 = ["a", "b", "c", "d"]

Split string as list

sentence = "the quick brown fox jumps over the lazy dog"
words = sentence.split()
print(words)

Filter positive numbers only - 1

numbers = [34.6, -203.4, 44.9, 68.3, -12.2, 44.6, 12.7]
newlist = []
for number in numbers:
    if number>0:
        newlist.append(number)
print(newlist)

Filter positive numbers only - 2

numbers = [34.6, -203.4, 44.9, 68.3, -12.2, 44.6, 12.7]
newlist = [int(x) for x in numbers if x > 0]
print(newlist)

Create word list from a sentence with no duplicate entries

set() removes all the duplicate entries in the array

strings = "my name is Chun Kang and Chun is my name"
r = set(strings.split())
print(r)

Tuple

The tuples cannot be changed unlike lists and tuples use parentheses, whereas lists use square brackets.

tup1 = ('physics', 'chemistry', 1997, 2000);
tup2 = (1, 2, 3, 4, 5 );
tup3 = "a", "b", "c", "d";

Set

Unordered collections of unique elements

Set(['Jane', 'Marvin', 'Janice', 'John', 'Jack'])
Set(['Janice', 'Jack', 'Sam'])
Set(['Jane', 'Zack', 'Jack'])
Set(['Jack', 'Sam', 'Jane', 'Marvin', 'Janice', 'John', 'Zack'])

Find overlapped entries from two arrays

a = set([ "Seoul", "Pusan", "Incheon", "Mokpo" ])
b = set([ "Seoul", "Incheon", "Suwon", "Daejeon", "Gwangjoo", "Taeku"])

print(a.intersection(b))
print(b.intersection(a))

The result will be like below

Result
{'Seoul', 'Incheon'} {'Seoul', 'Incheon'}

Find different elements from two arrays based on "symmetric_difference" method

a = set(["Jake", "John", "Eric"])
b = set(["John", "Jill"])

print(a.symmetric_difference(b))
print(b.symmetric_difference(a))

The result will be like below

Result
{'Jake', 'Eric', 'Jill'} {'Eric', 'Jake', 'Jill'}

Find different elements from two arrays based on "difference" method

a = set(["Jake", "John", "Eric"])
b = set(["John", "Jill"])

print(a.difference(b))
print(b.difference(a))

The result will be like below

Result
{'Jake', 'Eric'} {'Jill'}

Find different elements from two arrays based on "union" method

a = set(["Jake", "John", "Eric"])
b = set(["John", "Jill"])

print(a.union(b))

The result will be like below

Result
{'John', 'Eric', 'Jake', 'Jill'}

Print out a set containing all the participants from event A which did not attend event B

a = ["Jake", "John", "Eric"]
b = ["John", "Jill"]

print(set(a).difference(set(b)))

Dictionary

Python dictionaries are similar to lists in that they are mutable and can be nested to any arbitrary depth (constrained only by available memory).

A dictionary can contain any type of Python object, including another dictionary. The keys in a given dictionary do not need to be the same type as one another, nor do the values.

Dictionary elements are accessed by key. Unlike with list indexing, the order of the items in a dictionary plays no role in how the items are accessed.

Even though dictionary access does not rely on item order, as of version 3.7 the Python language specification does guarantee that the order of items in a dictionary is maintained once the dictionary is created.

dict = {'Name': 'Zara', 'Age': 7, 'Class': 'First'}

Get last name from full name by split()

The function can be easily implemented by string method

actor = {"name": "John Cleese", "rank": "awesome"}

def get_last_name():
    return actor["name"].split()[1]

get_last_name()
print("All exceptions caught! Good job!")
print("The actor's last name is %s" % get_last_name())

Accessing dictionary values

x = [
	'a',
	'b',
	{
		'foo': 1,
		'bar':
		{
			'x' : 10,
			'y' : 20,
			'z' : 30
		},
		'baz': 3
	},
	'c',
	'd'
]

print(x[2]['bar']['z'])

Result

Delete a dictionary element

Deleting a dictionary element by statement

del d['foo']

Deleting a dictionary element by method

d.pop('foo')

Generator

Random number generation

import random

def lottery():
    # returns 6 numbers between 1 and 40
    for i in range(6):
        yield random.randint(1, 40)

    # returns a 7th number between 1 and 15
    yield random.randint(1,15)

for random_number in lottery():
       print("And the next number is... %d!" %(random_number))

Swap variables' value

a = 1
b = 2
a, b = b, a
print(a,b)

Fibonacci series generator

The first two numbers of the series is always equal to 1, and each consecutive number returned is the sum of the last two numbers - the below code uses only two variables to get the result.

def fib():
    a, b = 1, 1
    while 1:
        yield a
        a, b = b, a + b

# testing code
import types
if type(fib()) == types.GeneratorType:
    print("Good, The fib function is a generator.")

    counter = 0
    for n in fib():
        print(n)
        counter += 1
        if counter == 10:
            break

Function Arguments(Parameters)

Multiple Function Argument recognition - the list of "therest" parameters

def foo(first, second, third, *therest):
    print("First: %s" %(first))
    print("Second: %s" %(second))
    print("Third: %s" %(third))
    print("And all the rest... %s" %(list(therest)))

foo(1,2,3,4,5)

Multiple Function Argument by keyword

def bar(first, second, third, **options):
    if options.get("action") == "sum":
        print("The sum is: %d" %(first + second + third))

    if options.get("number") == "first":
        return first

result = bar(1, 2, 3, action = "sum", number = "first")
print("Result: %d" %(result))

Regular Expression

RegEx(Regular Expressions) to search "[on]" or "[off]" on the string

import re

pattern = re.compile(r"\[(on|off)\]") # Slight optimization
print(re.search(pattern, "Mono: Playback 65 [75%] [-16.50dB] [on]"))

RegEx(Regular Expression) to check email address

import re

def test_email(your_pattern):
    pattern = re.compile(your_pattern)
    emails = ["john@example.com", "python-list@python.org", "wha.t.`1an?ug{}ly@email.com"]
    for email in emails:
        if not re.match(pattern, email):
            print("You failed to match %s" % (email))
        elif not your_pattern:
            print("Forgot to enter a pattern!")
        else:
            print("Pass")

pattern = r"[a-z0-9]+@[a-z0-9]+\.[a-z0-9]+"
test_email(pattern)

Exception Handling

try/except block

def do_stuff_with_number(n):
    print(n)

def catch_this():
    the_list = (1, 2, 3, 4, 5)

    for i in range(20):
        try:
            do_stuff_with_number(the_list[i])
        except IndexError: # Raised when accessing a non-existing index of a list
            do_stuff_with_number('out of bound - %d' % i)

catch_this()

Numpy

Convert arrays to Numpy arrays

# Create 2 new lists height and weight
height = [1.87,  1.87, 1.82, 1.91, 1.90, 1.85]
weight = [81.65, 97.52, 95.25, 92.98, 86.18, 88.45]

# Import the numpy package as np
import numpy as np

# Create 2 numpy arrays from height and weight
np_height = np.array(height)
np_weight = np.array(weight)

print(type(np_height))

# Calculate bmi
bmi = np_weight / np_height ** 2

# Print the result
print(bmi)

# For a boolean response
print(bmi > 23)

# Print only those observations above 23
print(bmi[bmi > 23])

Result

<class 'numpy.ndarray'>
[ 23.34925219  27.88755755  28.75558507  25.48723993  23.87257618
  25.84368152]
[ True  True  True  True  True  True]
[ 23.34925219  27.88755755  28.75558507  25.48723993  23.87257618
  25.84368152]

Convert all of the weights from kilograms to pounds based in NumPy

weight_kg = [81.65, 97.52, 95.25, 92.98, 86.18, 88.45]

import numpy as np

# Create a numpy array np_weight_kg from weight_kg
np_weight_kg = np.array(weight_kg)

# Create np_weight_lbs from np_weight_kg
np_weight_lbs = np_weight_kg * 2.2

# Print out np_weight_lbs
print(np_weight_lbs)

Result

    [ 179.63   214.544  209.55   204.556  189.596  194.59 ]

Pandas DataFrame / CSV / Join / Merge

Create a Pandas DataFrame based on array

dict = {"country": ["Brazil", "Russia", "India", "China", "South Africa"],
       "capital": ["Brasilia", "Moscow", "New Dehli", "Beijing", "Pretoria"],
       "area": [8.516, 17.10, 3.286, 9.597, 1.221],
       "population": [200.4, 143.5, 1252, 1357, 52.98] }

import pandas as pd
brics = pd.DataFrame(dict)
print(brics)

Adding index to a Pandas DataFrame

# Set the index for brics
brics.index = ["BR", "RU", "IN", "CH", "SA"]

# Print out brics with new index values
print(brics)

Reading CSV by Pandas DataFrame

# Import pandas as pd
import pandas as pd

# Import the cars.csv data: cars
cars = pd.read_csv('cars.csv')

# Print out cars
print(cars)

CSV

Reading a CSV file by Pandas DataFrame with 1st column as index

# Import pandas and cars.csv
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out country column as Pandas Series
print(cars['cars_per_cap'])

# Print out country column as Pandas DataFrame
print(cars[['cars_per_cap']])

# Print out DataFrame with country and drives_right columns
print(cars[['cars_per_cap', 'country']])

Save a Pandas DaraFrame by CSV format

dict = {"country": ["Brazil", "Russia", "India", "China", "South Africa"],
       "capital": ["Brasilia", "Moscow", "New Dehli", "Beijing", "Pretoria"],
       "area": [8.516, 17.10, 3.286, 9.597, 1.221],
       "population": [200.4, 143.5, 1252, 1357, 52.98] }

import pandas as pd
brics = pd.DataFrame(dict)

brics.to_csv('example.csv')

Save a Pandas DaraFrame by CSV format with header and no index

from pandas import DataFrame

Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
        'Price': [22000,25000,27000,35000]
        }

df = DataFrame(Cars, columns= ['Brand', 'Price'])

export_csv = df.to_csv (r'C:\Users\Ron\Desktop\export_dataframe.csv', index = None, header=True) #Don't forget to add '.csv' at the end of the path

print (df)

Print partial rows (observations) from a Pandas DataFrame

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out first 4 observations
print(cars[0:4])

# Print out fifth, sixth, and seventh observation
print(cars[4:6])

Data access by loc and iloc in Pandas DaraFrame - Select colums by index or name

loc is label-based, and iloc is integer index based

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out observation for Japan
print(cars.iloc[2])

# Print out observations for Australia and Egypt
print(cars.loc[['AUS', 'EG']])

Sort

Sort a Pandas DataFrame in an ascending order

df.sort_values(by=['Brand'], inplace=True)

# sort - ascending order
from pandas import DataFrame
 
Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
        'Price': [22000,25000,27000,35000],
        'Year': [2015,2013,2018,2018]
        }
 
df = DataFrame(Cars, columns= ['Brand', 'Price','Year'])

# sort Brand - ascending order
df.sort_values(by=['Brand'], inplace=True)

print (df)

Sort a Pandas DataFrame in a descending order

df.sort_values(by=['Brand'], inplace=True, ascending=False)

# sort - descending order
from pandas import DataFrame
 
Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
        'Price': [22000,25000,27000,35000],
        'Year': [2015,2013,2018,2018]
        }
 
df = DataFrame(Cars, columns= ['Brand', 'Price','Year'])

# sort Brand - descending order
df.sort_values(by=['Brand'], inplace=True, ascending=False)

print (df)

Sort a Pandas DataFrame by multiple columns

df.sort_values(by=['First Column','Second Column',...], inplace=True)

# sort by multiple columns
from pandas import DataFrame
 
Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
        'Price': [22000,25000,27000,35000],
        'Year': [2015,2013,2018,2018]
        }
 
df = DataFrame(Cars, columns= ['Brand', 'Price','Year'])

# sort by multiple columns: Year and Price
df.sort_values(by=['Year','Price'], inplace=True)

print (df)

Join and merge Pandas DataFrames

import pandas as pd
from IPython.display import display
from IPython.display import Image

raw_data = {
        'subject_id': ['1', '2', '3', '4', '5'],
        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], 
        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}
df_a = pd.DataFrame(raw_data, columns = ['subject_id', 'first_name', 'last_name'])

raw_data = {
        'subject_id': ['4', '5', '6', '7', '8'],
        'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], 
        'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}
df_b = pd.DataFrame(raw_data, columns = ['subject_id', 'first_name', 'last_name'])

raw_data = {
        'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],
        'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}
df_n = pd.DataFrame(raw_data, columns = ['subject_id','test_id'])

# Join the two dataframes along rows
df_new = pd.concat([df_a, df_b])

# Join the two dataframes along columns
pd.concat([df_a, df_b], axis=1)

# Merge two dataframes along the subject_id value
pd.merge(df_new, df_n, on='subject_id')

# Merge two dataframes with both the left and right dataframes using the subject_id key
pd.merge(df_new, df_n, left_on='subject_id', right_on='subject_id')

# Merge with outer join
pd.merge(df_a, df_b, on='subject_id', how='outer')

# Merge with inner join
pd.merge(df_a, df_b, on='subject_id', how='inner')

# Merge with right join
pd.merge(df_a, df_b, on='subject_id', how='right')

# Merge with left join
pd.merge(df_a, df_b, on='subject_id', how='left')

# Merge while adding a suffix to duplicate column names
pd.merge(df_a, df_b, on='subject_id', how='left', suffixes=('_left', '_right'))

# Merge based on indexes
pd.merge(df_a, df_b, right_index=True, left_index=True)

Get the maximum value of column in Pandas DataFrame

import pandas as pd
 
# Create a DataFrame
d = {
    'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine',
    'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'],
    'Age':[26,24,23,22,23,24,26,24,22,23,24,24],
 
    'Score':[85,63,55,74,31,77,85,63,42,62,89,77]
}
 
df = pd.DataFrame(d,columns=['Name','Age','Score'])

# get the maximum values of all the column in dataframe - it will be raghu, 26, 89, object
df.max()

# get the maximum value of the column 'Age' - it will be 26
df['Age'].max()

# get the maximum value of the column 'Name' - it will be raghu
df['Name'].max()

Get the minimum value of column in Pandas DataFrame

import pandas as pd
 
# Create a DataFrame
d = {
    'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine',
    'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'],
    'Age':[26,24,23,22,23,24,26,24,22,23,24,24],
 
    'Score':[85,63,55,74,31,77,85,63,42,62,89,77]
}
 
df = pd.DataFrame(d,columns=['Name','Age','Score'])

# get the minimum values of all the column in dataframe - it will display Alex, 22, 31, object
df.min()

# get the minimum value of the column 'Age' - it will be 22
df['Age'].min()

# get the minimum value of the column 'Name' - it will be Alex
df['Name'].min()

Select row with maximum and minimum value in Pandas DataFrame

import pandas as pd
 
# Create a DataFrame
d = {
    'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine',
            'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'],
    'Age':[26,24,23,22,23,24,26,24,22,23,24,24],
      
    'Score':[85,63,55,74,31,77,85,63,42,62,89,77]}
 
df = pd.DataFrame(d,columns=['Name','Age','Score'])

# get the row of max value
df.loc[df['Score'].idxmax()]

# get the row of minimum value
df.loc[df['Score'].idxmin()]

Get the unique values (rows) of a Pandas Dataframe

import pandas as pd
 
# Create a DataFrame
d = {
    'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine',
            'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'],
    'Age':[26,24,23,22,23,24,26,24,22,23,24,24]
}
 
df = pd.DataFrame(d,columns=['Name','Age'])

# get the unique values (rows)
print df.drop_duplicates()

# get the unique values (rows) by retaining last row
print df.drop_duplicates(keep='last')

Get the list of column headers or column name in a Pandas DataFrame

import pandas as pd
 
# Create a DataFrame
d = {
    'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine',
            'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'],
    'Age':[26,24,23,22,23,24,26,24,22,23,24,24],
      
       'Score':[85,63,55,74,31,77,85,63,42,62,89,77]}
 
df = pd.DataFrame(d,columns=['Name','Age','Score'])

# method 1: get list of column name
list(df.columns.values)

# method 2: get list of column name
list(df)

Delete or Drop the duplicate row of a Pandas DataFrame

import pandas as pd
 
# Create a DataFrame
d = {
    'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine',
            'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'],
    'Age':[26,24,23,22,23,24,26,24,22,23,24,24],
      
    'Score':[85,63,55,74,31,77,85,63,42,62,89,77]}
 
df = pd.DataFrame(d,columns=['Name','Age','Score'])

# drop duplicate rows
df.drop_duplicates()

# drop duplicate rows by retaining last occurrence
df.drop_duplicates(keep='last')

# drop duplicate by a column name
df.drop_duplicates(['Name'], keep='last')

Drop or delete the row in Pandas DataFrame with conditions

import pandas as pd
 
# Create a DataFrame
d = {
    'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine',
            'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'],
    'Age':[26,24,23,22,23,24,26,24,22,23,24,24],
      
       'Score':[85,63,55,74,31,77,85,63,42,62,89,77]}
 
df = pd.DataFrame(d,columns=['Name','Age','Score'])

# Drop an observation or row
df.drop([1,2])


# Drop a row by condition
df[df.Name != 'Alisa']

# Drop a row by index
df.drop(df.index[2])

# Drop bottom 3 rows
df[:-3]

Reshape wide to long in Pandas DataFrame with melt() function

import pandas as pd
 
# Create a DataFrame
d = {
    'countries':['A','B','C'],
    'population_in_million':[100,200,120],
    'gdp_percapita':[2000,7000,15000]
    }
 
df = pd.DataFrame(d,columns=['countries','population_in_million','gdp_percapita'])

# shape from wide to long with melt function in pandas
df2=pd.melt(df,id_vars=['countries'],var_name='metrics', value_name='values')

Reshape long to wide in Pandas DataFrame with pivot function

import pandas as pd
 
# Create a DataFrame
d = {
    'countries':['A','B','C','A','B','C'],
    'metrics':['population_in_million','population_in_million','population_in_million',
                             'gdp_percapita','gdp_percapita','gdp_percapita'],
    'values':[100,200,120,2000,7000,15000]
    }
 
df = pd.DataFrame(d,columns=['countries','metrics','values'])

# reshape from long to wide in pandas python
df2=df.pivot(index='countries', columns='metrics', values='values')

Reshape using Stack() and unstack() function in Pandas DataFrame

import pandas as pd
 
 
header = pd.MultiIndex.from_product([['Semester1','Semester2'],['Maths','Science']])
d=([[12,45,67,56],[78,89,45,67],[45,67,89,90],[67,44,56,55]])
 
 
df = pd.DataFrame(d,
                  index=['Alisa','Bobby','Cathrine','Jack'],
                  columns=header)

# stack the dataframe
stacked_df=df.stack()

# unstack the dataframe
unstacked_df = stacked_df.unstack()

# stack the dataframe of column at level 0
stacked_df_lvl=df.stack(level=0)

# unstack the dataframe
unstacked_df1 = stacked_df_lvl.unstack()

Space shortcuts

Page tree

Python Code Snipets

Basic Data Types

Constant Floating-point value

Basic Operators

Python Arithmetic Operators

Python Comparison Operators

Data Structures - List / Set / Tuple / Dictionary

List

Split string as list

Filter positive numbers only - 1

Filter positive numbers only - 2

Create word list from a sentence with no duplicate entries

Tuple

Set

Find overlapped entries from two arrays

Find different elements from two arrays based on "symmetric_difference" method

Find different elements from two arrays based on "difference" method

Find different elements from two arrays based on "union" method

Print out a set containing all the participants from event A which did not attend event B

Dictionary

Get last name from full name by split()

Accessing dictionary values

Delete a dictionary element

Generator

Random number generation

Swap variables' value

Fibonacci series generator

Function Arguments(Parameters)

Multiple Function Argument recognition - the list of "therest" parameters

Multiple Function Argument by keyword

Regular Expression

RegEx(Regular Expressions) to search "[on]" or "[off]" on the string

RegEx(Regular Expression) to check email address

Exception Handling

try/except block

Numpy

Convert arrays to Numpy arrays

Convert all of the weights from kilograms to pounds based in NumPy

Pandas DataFrame / CSV / Join / Merge

Create a Pandas DataFrame based on array

Adding index to a Pandas DataFrame

Reading CSV by Pandas DataFrame

CSV

Reading a CSV file by Pandas DataFrame with 1st column as index

Save a Pandas DaraFrame by CSV format

Save a Pandas DaraFrame by CSV format with header and no index

Print partial rows (observations) from a Pandas DataFrame

Data access by loc and iloc in Pandas DaraFrame - Select colums by index or name

Sort

Sort a Pandas DataFrame in an ascending order

Sort a Pandas DataFrame in a descending order

Sort a Pandas DataFrame by multiple columns

Join and merge Pandas DataFrames

Get the maximum value of column in Pandas DataFrame

Get the minimum value of column in Pandas DataFrame

Select row with maximum and minimum value in Pandas DataFrame

Get the unique values (rows) of a Pandas Dataframe

Get the list of column headers or column name in a Pandas DataFrame

Delete or Drop the duplicate row of a Pandas DataFrame

Drop or delete the row in Pandas DataFrame with conditions

Reshape wide to long in Pandas DataFrame with melt() function

Reshape long to wide in Pandas DataFrame with pivot function

Reshape using Stack() and unstack() function in Pandas DataFrame