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Python Pandas – How to use Pandas DataFrame Property: shape
Write a Python program to read data from the products.csv file and print the number of rows and columns. Then print the ‘product’ column value matches ‘Car’ for first ten rows
Assume, you have ‘products.csv’ file and the result for number of rows and columns and ‘product’ column value matches ‘Car’ for first ten rows are −
Rows: 100 Columns: 8 id product engine avgmileage price height_mm width_mm productionYear 1 2 Car Diesel 21 16500 1530 1735 2020 4 5 Car Gas 18 17450 1530 1780 2018 5 6 Car Gas 19 15250 1530 1790 2019 8 9 Car Diesel 23 16925 1530 1800 2018
We have two different solutions for this problem.
Solution 1
Read data from products.csv file and assign to df
df = pd.read_csv('products.csv ')Print the number of rows = df.shape[0] and columns = df.shape[1]
Set df1 to filter first ten rows from df using iloc[0:10,:]
df1 = df.iloc[0:10,:]
Calculate the product column values matches to car using df1.iloc[:,1]
Here, product column index is 1 and finally print the data
df1[df1.iloc[:,1]=='Car']
Example
Let’s check the following code to get a better understanding −
import pandas as pd
df = pd.read_csv('products.csv ')
print("Rows:",df.shape[0],"Columns:",df.shape[1])
df1 = df.iloc[0:10,:]
print(df1[df1.iloc[:,1]=='Car'])Output
Rows: 100 Columns: 8 id product engine avgmileage price height_mm width_mm productionYear 1 2 Car Diesel 21 16500 1530 1735 2020 4 5 Car Gas 18 17450 1530 1780 2018 5 6 Car Gas 19 15250 1530 1790 2019 8 9 Car Diesel 23 16925 1530 1800 2018
Solution 2
Read data from products.csv file and assign to df
df = pd.read_csv('products.csv ')Print the number of rows = df.shape[0] and columns = df.shape[1]
Take first ten rows using df.head(10) and assign to df
df1 = df.head(10)
Take product column values matches to Car using below method
df1[df1['product']=='Car']
Now, let’s check its implementation to get a better understanding −
Example
import pandas as pd
df = pd.read_csv('products.csv ')
print("Rows:",df.shape[0],"Columns:",df.shape[1])
df1 = df.head(10)
print(df1[df1['product']=='Car'])Output
Rows: 100 Columns: 8 id product engine avgmileage price height_mm width_mm productionYear 1 2 Car Diesel 21 16500 1530 1735 2020 4 5 Car Gas 18 17450 1530 1780 2018 5 6 Car Gas 19 15250 1530 1790 2019 8 9 Car Diesel 23 16925 1530 1800 2018
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