Pandas: Selecting Entries

Selecting Entries

• Following examples illustrate the indexing and selection of elements ‒

Python program

import pandas as pd

data = [10,20,30,40,50]

x = pd.Series(data, index=["a","b","c","d","e"], dtype =  float)

print(x)

print("The 3rd element")

print(x["c"])

print(x[2])

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

print("The second, third and fourth element")

print(x[1:4])

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

print("The second, fourth, and fifth element")

print(x[[1,3,4]])

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

print("First two elements")

print(x[:2])

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

Output

Selection with loc and iloc

The loc and iloc are two functions in Pandas that are used to slice a data set in a Pandas DataFrame.

The loc and iloc functions are commonly used to select certain groups of rows (and columns) of a pandas DataFrame.

The loc method is used to select the data from the dataframe. Using this function we can pass the condition for selection of data.

Demo example

Consider a students data frame created using Pandas. It is as follows‒

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)

Now we will apply loc methods to select the desired data from the data set.

1) Selecting a single row

We can select a single row using the index label.

print(df.loc['s4'])

With .loc[], you specify the column label directly, while with .iloc[ ], we use the column index.

print(df.iloc[3])

2) Selection multiple rows

For selecting multiple rows, we can specify multiple labels. To select multiple values using loc[ ] and iloc[ ], we can specify the rows and columns you want to select. For instance ‒ print(df.loc[['s3','s5']])

or we can specify the column names

print(df.loc[:,["Names", "Courses"]])

3) Selection based on condition

We can specify the condition to loc function and based on that condition desired data can

be selected.

print(df.loc[df['Fees'] >=20000])

or

print(df.loc[df['Fees']>=20000])

The complete Python code is as follows

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("‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒")

print("Selecting Single row")

print(df.loc['s4'])

print("#################################")

print(df.iloc[3])

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

print("Selecting multiple rows")

print(df.loc[['s3','s5']])

print("#################################")

print(df.iloc[[2,3]])

print("************************************************")

print(df.loc[:,["Names","Courses"]])

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

print("Selecting rows based on Condition")

print(df.loc[df['Fees']>=20000])

print("#################################")

print(df.loc[list(df['Fees']>=20000)])

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

Output