Pandas allow us to get the shape of the dataframe by counting the numbers of rows and columns in the dataframe. You can try various approaches to get the number of rows and columns of the dataframe. All of them have been discussed below.
Example: Let’s take an example of a dataframe which consists of data of exam result of students.
# importing pandas import pandas as pd result_data = { 'name' : [ 'Katherine' , 'James' , 'Emily' , 'Michael' , 'Matthew' , 'Laura' ], 'score' : [ 98 , 80 , 60 , 85 , 49 , 92 ], 'age' : [ 20 , 25 , 22 , 24 , 21 , 20 ], 'qualify_label' : [ 'yes' , 'yes' , 'no' , 'yes' , 'no' , 'yes' ]} # creating dataframe df = pd.DataFrame(result_data, index = None ) # computing number of rows rows = len (df.axes[ 0 ]) # computing number of columns cols = len (df.axes[ 1 ]) print (df) print ( "Number of Rows: " , rows) print ( "Number of Columns: " , cols) |
Output :
Example: Let’s take an example of a dataframe that consists of data on product sales.
# importing pandas import pandas as pd # creating dataframe df = pd.DataFrame({ 'Product' : [ 'Carrots' , 'Broccoli' , 'Banana' , 'Banana' , 'Beans' , 'Orange' , 'Broccoli' , 'Banana' ], 'Category' : [ 'Vegetable' , 'Vegetable' , 'Fruit' , 'Fruit' , 'Vegetable' , 'Fruit' , 'Vegetable' , 'Fruit' ], 'Quantity' : [ 8 , 5 , 3 , 4 , 5 , 9 , 11 , 8 ], 'Amount' : [ 270 , 239 , 617 , 384 , 626 , 610 , 62 , 90 ]}) print (df) # getting number of rows print ( "Number of Rows: " , len (df.axes[ 0 ])) # getting number of columns print ( "Number of Columns: " , len (df.axes[ 1 ])) |
Output :
Example: Here, we will try a different approach for calculating rows and columns of a dataframe of imported csv file.
# importing pandas import pandas as pd # importing csv file print (df.head()) # obtaining the shape print ( "shape of dataframe" , df.shape) # obtaining the number of rows print ( "number of rows : " , df.shape[ 0 ]) # obtaining the number of columns print ( "number of columns : " , df.shape[ 1 ]) |
Output :
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