Pandas: Sort and Rank

Sort and Rank

1) Sorting

The sort_index() function is used to sort series by index labels

Python program

import pandas as pd

s = pd.Series(["Archana", "Varsha","Rashmi","Usha"],index=[3,1,4,2])

print("‒‒‒‒‒‒‒‒‒‒‒‒Before‒‒‒‒‒‒‒‒‒‒‒‒‒")

print(s)

print("‒‒‒‒‒‒‒‒ After ‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒")

print(s.sort_index())

Output

For displaying the data in descending order we write

print(s.sort_index(ascending=False))

We can also sort the data by values. The sort_values() function is used for that,

Syntax

dataframe.sort_values(by, axis, ascending, inplace, kind, na_position, ignore_index, key)

•  by: It specifies labels to sort by bq es asbasq hoqmi

•  axis: The values can be 0,1,'index', 'columns'. The default 0. The axis to be sorted.

•  ascending: bool or list of bool, default True. Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.

•  inplace: bool, default False. If True, perform operation in‒place

•  kind: {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'. Choice of sorting algorithm. See also ndarray.np.sort for more information. Mergesort is the only stable algorithm. For DataFrames, this option is only applied when sorting on a single column or label.

• na_position : {'first', 'last'}, default 'last'. first puts NaNs at the beginning, last puts NaNs at the end.

Python code

import pandas as pd

students = {

'Names':["Vedant","Mayuresh","Ishwari","Himani","Varad","Aakash"],

'Courses':["Python","Java", "DevOps", "Hadoop","FullStack","Blockchain"],

'Fees':[20000,10000,15000,14000,15000,21000],

'Duration': ['40days', '60days', '60days','40days', '90days', '80days']

}

index_labels = ['s1','s2', 's3','s4', 's5','s6']

df = pd.DataFrame(students, index=index_labels)

print(df)

print("‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒Sorting by Names‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒")

df1 = df.sort_values(by='Names')

print(df1)

print("‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒Sorting by Courses‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒")

df2 = df.sort_values(by='Fees')

print(df2)

Output

2) Ranking

The Dataframe.rank() function of Pandas is used to rank the data in different ways. After sorting (by default in ascending order), the position is used to determine the rank that is returned. If data contains equal values, then they are assigned with the average of the ranks of each value by default.

The string values are ranked alphabetically in ascending order if there is any missing value, they are ignored in the ranking and ranked as NaN.

Following example illustrates the use of rank() function.

Python code

import pandas as pd

persons = {

'Names': ["Vedant","Mayuresh", "Ishwari", "Himani", "Varad","Aakash"]

}

index labels = ['s1','s2','s3°, 's4','s5','s6']

df = pd.DataFrame(persons,index=index_labels)

print(df)

df['Ranked_Names']=df['Names'].rank()

print("Ranking of Pandas Dataframe Names Column:\n",df)

Output

Code explanation: In above code,

We have created a data frame containing some names. Then we have applied rank( ) function on the names column. The names are ranked alphabetically in ascending order. Another column named Ranked_Names is created and the corresponding ranks are stored in that columns.

For instance the name "Akash" has a rank 1.0, "Himani" has a rank 2.0 because alphabetically A comes before H.