How can I count frequency of all values in several csv columns

Question

I have several csv columns that store lottery numbers and some other info, like date when the number was drawn. I need to get a dictionary as my output. So far I have been able to print number of occurrences in each column individually. # Import libraries import pandas as pd from IPython.display import display # Turn csv file into a pandas dataframe df = pd.read_csv("LOTTOMAX.csv") # Only select columns that I'm interested in. Csv file contains additional useless info. selection = df[['NUMBER DRAWN 1', 'NUMBER DRAWN 2', 'NUMBER DRAWN 3', 'NUMBER DRAWN 4', 'NUMBER DRAWN 5', 'NUMBER DRAWN 6', 'NUMBER DRAWN 7']] # Loop over columns and apply value_counts(). Output to terminal. for col in selection.columns: # I have included this to make terminal output more readable. print('-' * 40 + col + '-' * 40 , end='\n') display(selection[col].value_counts().to_string()) JavaScript questions and answers, JavaScript questions pdf, JavaScript question bank, JavaScript questions and answers pdf, mcq on JavaScript pdf, JavaScript questions and solutions, JavaScript mcq Test , Interview JavaScript questions, JavaScript Questions for Interview, JavaScript MCQ (Multiple Choice Questions)

Answer 1

I did this project for fun. Wanted to replicate a feauture on bclc website. Perhaps this will help someone. # Import libraries import pandas as pd from collections import Counter import matplotlib.pyplot as plt # Read csv file df = pd.read_csv("LOTTOMAX.csv") #csv file https://www.playnow.com/resources/documents/downloadable-numbers/LOTTOMAX.zip cols = ['NUMBER DRAWN 1', 'NUMBER DRAWN 2', 'NUMBER DRAWN 3', 'NUMBER DRAWN 4', 'NUMBER DRAWN 5', 'NUMBER DRAWN 6', 'NUMBER DRAWN 7'] results = [] # Add data to a list for i in cols: results += df[i].tolist() # Count occurrences occurr = Counter(results) # Display histogram plt.bar(list(occurr.keys()), occurr.values(), color='g') plt.xlabel("Numbers Drawn") plt.ylabel("Frequency") plt.show() This solution is imperfect, but it works.