Pupli

len(gapminder['country'].unique().tolist())

set(df['region'].values.tolist())

# Create a list of unique values by turning the
# pandas column into a set
list(set(df.trucks))

# Create a list of unique values in df.trucks
list(df['trucks'].unique())

# Import pandas package 
import pandas as pd 

# create a dictionary with five fields each 
data = { 
  'A':['A1', 'A2', 'A3', 'A4', 'A5'], 
  'B':['B1', 'B2', 'B3', 'B4', 'B4'], 
  'C':['C1', 'C2', 'C3', 'C3', 'C3'], 
  'D':['D1', 'D2', 'D2', 'D2', 'D2'], 
  'E':['E1', 'E1', 'E1', 'E1', 'E1'] } 

# Convert the dictionary into DataFrame 
df = pd.DataFrame(data) 

# Get the unique values of 'B' column 
df.B.unique() 

# Import pandas package 
import pandas as pd 

# create a dictionary with five fields each 
data = { 
  'A':['A1', 'A2', 'A3', 'A4', 'A5'], 
  'B':['B1', 'B2', 'B3', 'B4', 'B4'], 
  'C':['C1', 'C2', 'C3', 'C3', 'C3'], 
  'D':['D1', 'D2', 'D2', 'D2', 'D2'], 
  'E':['E1', 'E1', 'E1', 'E1', 'E1'] } 

# Convert the dictionary into DataFrame 
df = pd.DataFrame(data) 

# Get number of unique values in column 'C' 
df.C.nunique(dropna = True) 

References
https://pythonprogramming.net/graph-visualization-python3-pandas-data-analysis/
https://www.geeksforgeeks.org/get-unique-values-from-a-column-in-pandas-dataframe/
https://chrisalbon.com/python/data_wrangling/pandas_find_unique_values/
https://cmdlinetips.com/2018/01/how-to-get-unique-values-from-a-column-in-pandas-data-frame/

DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')

Sort Dataframe rows based on a single column

# Sort the rows of dataframe by column 'Name'
dfObj = dfObj.sort_values(by ='Name' )
print("Contents of Sorted Dataframe based on a single column 'Name' : ")
print(dfObj)

Sort Dataframe rows based on a multiple columns

dfObj = dfObj.sort_values(by =['Name', 'Marks'])
print("Contents of a Sorted Dataframe based on multiple columns 'Name' & 'Marks' : ")
print(dfObj)

Sort Dataframe rows based on columns in Descending Order

# Sort the rows of dataframe by column 'Name' in descending order
dfObj = dfObj.sort_values(by ='Name' , ascending=False)
print("Contents of Sorted Dataframe based on a column 'Name' in Descending Order : ")
print(dfObj)

Sort Dataframe rows based on a column in Place

# Sort the rows of dataframe by column 'Name' inplace
dfObj.sort_values(by='Name' , inplace=True)
print("Contents of Sorted Dataframe based on a single column 'Name' inplace: ")
print(dfObj)

Sort columns of a Dataframe based on a single row

dfObj = dfObj.sort_values(by ='b', axis=1)
print("Contents of Sorted Dataframe based on a single row index label 'b' ")
print(dfObj)

Sort columns of a Dataframe in Descending Order based on a single row

dfObj = dfObj.sort_values(by ='b', axis=1)
print("Contents of Sorted Dataframe based on a single row index label 'b' ")
print(dfObj)

Sort columns of a Dataframe based on a multiple rows

dfObj = dfObj.sort_values(by ='b', axis=1)
print("Contents of Sorted Dataframe based on a single row index label 'b' ")
print(dfObj)

Complete example is as follows:

import pandas as pd
def main():
# List of Tuples
students = [ ('Jack', 34, 'Sydney') ,
('Riti', 41, 'Delhi' ) ,
('Aadi', 16, 'New York') ,
('Riti', 22, 'Delhi' ) ,
('Riti', 35, 'Delhi' ) ,
('Riti', 40, 'Mumbai' )
]
# Create a DataFrame object
dfObj = pd.DataFrame(students, columns=['Name', 'Marks', 'City'], index=['b', 'a', 'f', 'e', 'd', 'c'])
print("Original Dataframe : ")
print(dfObj)
print('**** Sort Dataframe rows based on a single column ****')
# Sort the rows of dataframe by column 'Name'
dfObj = dfObj.sort_values(by ='Name' )
print("Contents of Sorted Dataframe based on a single column 'Name' : ")
print(dfObj)
print('**** Sort Dataframe rows based on a multiple columns ****')
dfObj = dfObj.sort_values(by =['Name', 'Marks'])
print("Contents of a Sorted Dataframe based on multiple columns 'Name' & 'Marks' : ")
print(dfObj)
print('**** Sort Dataframe rows based on a single column in Descending Order ****')
# Sort the rows of dataframe by column 'Name' in descending order
dfObj = dfObj.sort_values(by ='Name' , ascending=False)
print("Contents of Sorted Dataframe based on a column 'Name' in Descending Order : ")
print(dfObj)
print('**** Sort Dataframe rows based on a single column in place ****')
# Sort the rows of dataframe by column 'Name' inplace
dfObj.sort_values(by='Name' , inplace=True)
print("Contents of Sorted Dataframe based on a single column 'Name' inplace: ")
print(dfObj)
print('******** Sort columns of Dataframe based on a single or multiple rows ********')
# List of Tuples
matrix = [(222, 16, 23),
(333, 31, 11),
(444, 34, 11),
]
# Create a DataFrame object of 3X3 Matrix
dfObj = pd.DataFrame(matrix, index=list('abc'))
print("Original Dataframe: ")
print(dfObj)
# Sort columns of a dataframe based on a single row with index label 'b'
dfObj = dfObj.sort_values(by ='b', axis=1)
print("Contents of Sorted Dataframe based on a single row index label 'b' ")
print(dfObj)
# Sort columns of a dataframe in descending order based on a single row with index label 'b'
dfObj = dfObj.sort_values(by='b', axis=1, ascending=False)
print("Contents of Sorted Dataframe in descending order based on a single row index label 'b' ")
print(dfObj)
# Sort columns of a dataframe based on a multiple row with index labels 'b' & 'c'
dfObj = dfObj.sort_values(by =['b' , 'c' ], axis=1)
print("Contents of Sorted Dataframe based on multiple rows index label 'b' & 'c' ")
print(dfObj)
if __name__ == '__main__':
main()

References
https://thispointer.com/pandas-sort-rows-or-columns-in-dataframe-based-on-values-using-dataframe-sort_values/
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html