  # Select row with max and min value in Pandas dataframe

NumPy | Python Methods and Functions

Consider this dataset.

Output: ### Using max on Dataframe —

Code # 1: shows the maximum of Driver, Points, Age columns.

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 ` # pandas and numpy imports ` ` import ` ` pandas as pd ` ` import ` ` numpy as np `   ` # 2018 Driving World Championship data ` ` dict1 ` ` = ` ` {` `` Driver` ` `: [` ` `Hamilton` ` `, ` ` `Vettel` ` `, ` `` Raikkonen` ` `, ` ` ` `` Verstappen` ` `, ` ` `Bottas` ` `, ` ` `Ricciardo` ` `, ` ` `Hulkenberg` ` `, ` ` `Perez` ` `, ` `` Magnussen` ` `, ` ` `Sainz` ` `, ` ` `Alonso` ` `, ` `` Ocon` ` `, ` ` `Leclerc` ` `, ` ` ` ` `Grosjean` ` `, ` `` Gasly` ` `, ` ` `Vandoorne` ` `, ` ` ` ` `Ericsson` ` `, ` `` Stroll` ` `, ` ` `Hartley` , `Sirotkin` ], ``   `Points` : [ 408 , 320 , 251 , 249 , 247 , 170 , 69 , 62 , 56 , 53 , 50 , 49 , 39 , 37 , 29 , 12 , 9 , 6 , 4 , 1 ],   `Age` : [ 33 , 31 , 39 , 21 , 29 , 29 , 31 , 28 , 26 , 24 , 37 , 22 , 21 , 32 , 22 , 26 , 28 , 20 , 29 , 23 ]}    # create a data frame using the DataFrame constructor df = pd.DataFrame (dict1) print (df. head ( 10 )) `
 ` # pandas and numpy imports ` ` import ` ` pandas as pd ` ` import ` ` numpy as np `   ` # 2018 Driving World Championship data ` ` dict1 ` ` = { `Driver` : [ `Hamilton` , ` Vettel` , `Raikkonen` , ``   `Verstappen` , ` Bottas` , `Ricciardo` ,   `Hulkenberg` , ` Perez` , ` Magnussen` ,    `Sainz` , ` Alonso` , `Ocon` , ` Leclerc` , `Grosjean` , `Gasly` , ` Vandoorne` ,   ` Ericsson` , `Stroll` , ` Hartley` , `Sirotkin` ],   `Points` : [ 408 , 320 , 251 , 249 , 247 , 170 , 69 , 62 , 56 , 53 , 50 , 49 , 39 , 37 , 29 , 12 , 9 , 6 , 4 , 1 ],   `Age` : [ 33 , 31 , 39 , 21 , 29 , 29 , 31 , 28 , 26 , 24 , 37 ,   22 , 21 , 32 , 22 , 26 , 28 , 20 , 29 , 23 ]}    # create a data frame using the DataFrame constructor df = pd.DataFrame (dict1)   # the result shows a maximum of # Driver, Points, Age columns. print (df. max ()) `
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Output: Code # 2: Who scored maximum points

 ` # pandas and numpy imports ` ` import ` ` pandas as pd ` ` import ` ` numpy as np `   ` # 2018 Driving World Championship data ` ` dict1 = { `Driver` : [ `Hamilton` , ` ` `Vettel` ` `, ` `` Raikkonen `` `, ` ` ` `` Verstappen` ` `, ` ` `Bottas` ` `, ` `` Ricciardo` ` `, ` ` `Hulkenberg` ` `, ` `` Perez` ` `, ` ` `Magnussen` ` `, ` ` ` ` `Sainz` ` `, ` `` Alonso` ` `, ` ` `Ocon` ` `, ` `` Leclerc` ` `, ` `  `Grosjean` , ` Gasly` , ` Vandoorne` , `` ` Ericsson` , `Stroll` , `Hartley` , ` Sirotkin ` ],    `Points` : [ 408 , 320 , 251 , 249 , 247 , 170 , 69 , 62 , 56 , 53 , 50 , 49 , 39 , 37 , 29 , 12 , 9 , 6 , 4 , 1 ],   `Age` : [ 33 , 31 , 39 , 21 , 29 , 29 , 31 , 28 , 26 , 24 , 37 , 22 , 21 , 32 , 22 , 26 , 28 , 20 , 29 , 23 ]}   # create a data frame using the constructor DataFrame df = pd.DataFrame ( dict1)   # Who scored more points? print (df [df.Points = = df.Points. max ( )]) `

Output: Code # 3: What is the maximum age

 ` # pandas and numpy imports ` ` import ` ` pandas as pd ` ` import ` ` numpy as np ` ` `  ` # 2018 Driving World Championship data ` ` dict1 ` ` = ` ` {` ` `Driver` ` `: [` ``Hamilton` ` `, ` `` Vettel` ` `, ` ` `Raikkonen` ` `, ` ` ` ` `Verstappen` ` `, ` `` Bottas` ` `, ` ` `Ricciardo` ` `, ` ` ` ` `Hulkenberg` ` `, ` `` Perez` ` `, ` ` `Magnussen` ` `, ` ` ` ` `Sainz` ` `, ` `` Alonso` ` `, ` ` `Ocon` ` `, ` `` Leclerc` ` `, ` ` ` `` Grosjean` ` `, ` ` `Gasly` , `Vandoorne` , `` `Ericsson` , ` Stroll ` , ` Hartley` , `Sirotkin` ],    `Points` : [ 408 , 320 , 251 , 249 , 247 , 170 , 69 , 62 , 56 , 53 , 50 , 49 , 39 , 37 , 29 , 12 , 9 , 6 , 4 , 1 ],   `Age` : [ 33 , 31 , 39 , 21 , 29 , 29 , 31 , 28 , 26 , 24 , 37 , 22 , 21 , 32 , 22 , 26 , 28 , 20 , 29 , 23 ]}   # create a data frame using the DataFrame constructor df = pd.DataFrame (dict1)   # what is the maximum age? print (df.Age. max ()) `

Output: Code # 4: Which line has the maximum age in the data frame | who is the oldest driver?

 ` # pandas and numpy imports ` ` import ` ` pandas as pd ` ` import ` ` numpy as np `   ` # 2018 Driving World Championship data ` ` dict1 ` ` = ` ` {` ` `Driver` ` `: [` `` Hamilton` ` `, ` ` `Vettel` ` `, ` `` Raikkonen` ` ` , ` ` `Verstappen` ` `, ` ` `Bottas` ` `, ` ` `Ricciardo` ` `, ` ` `Hulkenberg` ` `, ` `` Perez` ` `, ` ` `Magnussen` ` `, ` ` `Sainz` ` `, ` ` `Alonso` ` `, ` `` Ocon` ` `, ` ` `Leclerc` ` `, ` ` ` ` `Grosjean` ` `, ` `` Gasly` ` `, ` ` `Vandoorne` ` `, ` ` ` ` `Ericsson` ` `, ` ` `Stroll` ` `, ` `` Hartley` ` `, ` `` Sirotkin` ` `], `   ` ` `` Points` ` ` : [` ` 408 ` `, ` ` 320 ` `, ` ` 251 ` `, ` ` 249 ` `, ` ` 247 ` `, ` ` 170 ` `, ` ` 69 ` `, ` ` 62 , 56 , `` 53 , 50 , 49 , 39 , 37 , 29 , 12 , 9 , 6 , 4 , 1 ],      `Age` : [ 33 , 31 , 39 , 21 , 29 , 29 , 31 , 28 , 26 , 24 , 37 , 22 , 21 , 32 , 22 , 26 , 28 , 20 , 29 , 23 ]}   # create a data frame using the DataFrame constructor df = pd.DataFrame (dict1)   # Which one the row has a maximum age | # who is the oldest driver? print (df [df.Age = = df.Age. max ()]) `

Output: ### Using min on Dataframe —

Code # 1: shows the minimum number of Driver columns, Points "," Age ".

` # pandas and numpy imports `

` import ` ` pandas as pd `

` import ` ` numpy as np `

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