Page 47 - Informatics_Practices_Fliipbook_Class12
P. 47
output:
CustomerName ItemPurchased Cost
0 Ashish Bread 22.5
1 Nikita Vegetables 90.0
2 Vinod Milk 75.0
The attribute dtypes is used to retrieve the type of objects in various columns of a DataFrame, as shown below:
>>> purchasesDF.dtypes
output:
CustomerName object
ItemPurchased object
Cost float64
dtype: object
C T 01 1. Consider the following nested list:
accounts = [[32345566, 'Savings', 5000], [43434567, 'Savings', 2000], [89897878, 'Current', -1000],
[67675454, 'Current', 10000]]
Write a statement that creates the following DataFrame with proper column labels:
Account Number Account Type Balance
0 32345566 Savings 5000
1 43434567 Savings 2000
2 89897878 Current -1000
3 67675454 Current 10000
2. For the DataFrame created in the previous question, display the type of values stored in each column.
Pandas DataFrame: Two-dimensional tabular structure that can accommodate objects of various types.
Dictionary, series or list of lists can be used to create Pandas DataFrame using pd.DataFrame() method.
2.3 Reading from csv File
In the above discussion, we have described how to create a DataFrame using a dictionary, list of lists, or a series.
Although simple, these methods work only when we are dealing with a very small quantity of data. The data is often
available in the form of spreadsheets (CSV files) that stores the data as comma-separated values. Many financial and
accounting systems utilize CSV files for data exchange. Even several e-commerce platforms use CSV files to import and
export product catalogues, update inventory, or export sales data.
To analyze and manipulate the data, we can read these CSV files in Pandas DataFrame using the method
read_csv() of Pandas as shown below:
>>> import pandas as pd
>>> #Reading a CSV file into a DataFrame
>>> groceryDF = pd.read_csv('Grocery.csv')
>>> print(groceryDF)
output:
Product Category Price Quantity
0 Bread Food 20 2
1 Milk Food 60 5
2 Biscuit Food 20 2
3 Bourn-Vita Food 70 1
4 Soap Hygiene 40 4
5 Brush Hygiene 30 2
6 Detergent Household 80 1
7 Tissues Hygiene 30 5
Data Handling using Pandas DataFrame 33

