AttributeError: 'DataFrame' object has no attribute 'Kumasi' . I want to calculate average sales per market from a large data in Jupyter Notebook

Question

I loaded a csv file directly from the jupyter notebook with the following code:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import seaborn as sns
sns.set()

from sklearn.preprocessing import LabelEncoder

sales_data = pd.read_csv('Buymore_sales_data.csv')
sales_data.head(13801)

I had an output with some of the data not shown even though the output gave me the length of the data as 13799 rows × 7 columns.

Link to the output screenshot - salesdataset

However, I want to calculate average sales per market since each market appears more than two times. To achieve this I wrote this code:

sales_data.Kumasi.Sales.mean()

After running this, I'm having an error message as

-------------------------------------------------------------
  AttributeError           Traceback (most recent call last)
  Input In [48], in <cell line: 2>()
  1 # compute average sales per market
  ----> 2 sales_data.Kumasi.Sales.mean()

  File ~\anaconda3\lib\site-packages\pandas\core\generic.py:5575, in 
  NDFrame.__getattr__(self, name)
   5568 if (
   5569     name not in self._internal_names_set
   5570     and name not in self._metadata
   5571     and name not in self._accessors
   5572     and self._info_axis._can_hold_identifiers_and_holds_name(name)
   5573 ):
   5574     return self[name]
   -> 5575 return object.__getattribute__(self, name)

   AttributeError: 'DataFrame' object has no attribute 'Kumasi'

Please what can I do?

• First of all, I want to see the entire dataset loaded in Jupyter Notebook
• Then I want to be able to calculate the average sales per market if possible

Answer 1

For seeing the entire table,

pd.set_option('display.max_columns', None)   
pd.set_option('display.max_rows', None)
# Instead of using None, you can specify what actually number you want it to.

For calculation,

sales_data[sales_data['Market'] == 'Kumasi']['Sales'].mean()

# Or

sales_data.query('Market == "Kumasi"')['Sales'].mean()