README.md

Preppin' Data with Python 🐍, R 🏴‍☠️, SQL ❄️, Alteryx ⬆️, and Tableau Prep ✨

Solving Preppin' Data Challenges with Python, and now Alteryx and Tableau Prep.

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🅰️ About

At the start of 2021 I wanted to improve my data prep skills with Python. Now in 2022 I'm doing the same for Alteryx and Tableau Prep.

Preppin' Data is a weekly data prep challenge built around Tableau Prep but the challenges apply to other tools and even coding languages, so ideal practice if you're looking to improve your data prep skills.

From participating, I'm much more confident in Python and use Python in projects such as working out the life expectancy of chess pieces and finding the resale value of Pokemon trading cards.

Below are my solutions and Python code snippets I regularly use in these challenges.

✅ 2023 Solutions

Challenge	Python	R	SQL
Week 01	🐍	🏴‍☠️	❄️
Week 02	🐍	🏴‍☠️	❄️
Week 03	🐍	🏴‍☠️	❄️
Week 04	🐍	🏴‍☠️	❄️
Week 05			❄️
Week 06			❄️
Week 07			❄️
Week 08			❄️
Week 09			❄️
Week 10			❄️
Week 11			❄️
Week 12			❄️

✅ 2022 Solutions

Challenge	Python	Alteryx	Tableau Prep
Week 01	🐍	⬆️	✨
Week 02	🐍	⬆️	✨
Week 03	🐍	⬆️	✨
Week 04	🐍	⬆️	✨

✅ 2021 Solutions

Challenge	Solution	Challenge	Solution	Challenge	Solution	Challenge	Solution
Week 01	Python	Week 14	Python	Week 27	Python	Week 40	Python
Week 02	Python	Week 15	Python	Week 28	Python	Week 41	Python
Week 03	Python	Week 16	Python	Week 29	Python	Week 42	Python
Week 04	Python	Week 17	Python	Week 30	Python	Week 43	Python
Week 05	Python	Week 18	Python	Week 31	Python	Week 44	Python
Week 06	Python	Week 19	Python	Week 32	Python	Week 45	Python
Week 07	Python	Week 20	Python	Week 33	Python	Week 46	Python
Week 08	Python	Week 21	Python	Week 34	Python	Week 47	Python
Week 09	Python	Week 22	Python	Week 35	Python	Week 48	Python
Week 10	Python	Week 23	Python	Week 36	Python	Week 49	Python
Week 11	Python	Week 24	Python	Week 37	Python	Week 50	Python
Week 12	Python	Week 25	Python	Week 38	Python	Week 51	Python
Week 13	Python	Week 26	Python	Week 39	Python	Week 52	Python

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🐍 Python Snippets

Reading Files

Reading csv files | Example: W05 2021

import pandas as pd

df = pd.read_csv('folder\\filename.csv')

Reading Excel files | Example: W04 2021

import pandas as pd

df = pd.read_excel('folder\\filename.xlsx', engine='openpyxl', sheet_name = 'Sheet1')

Reading and aggregrating multiple Excel tabs | Example: W21 2021

import pandas as pd

# Read all Excel tabs and concat as one dateframe
all_tabs = pd.read_excel('folder\\filename.xlsx', sheet_name=None)

# Bring all the sheets together
all_dfs = []
for tab_name, df in all_tabs.items():
    df['sheet_name'] = tab_name
    all_dfs.append(df)
    combined_df = pd.concat(all_dfs, ignore_index=True)

Skipping rows and columns in an Excel tab | Example: W48 2021

import pandas as pd

df = pd.read_excel('folder\\filename.xlsx', engine='openpyxl', sheet_name='Sheet1',nrows= 3,skiprows = range(1,7), usecols = "B:D")

Writing Files

Writing csv files with utf-8 encoding | Example: W10 2021

import pandas as pd

df.to_csv('folder\\filename.csv', encoding='utf-8-sig', index=False)

Writing Excel files | Example: W14 2021

import pandas as pd

with pd.ExcelWriter('folder\\filename.xlsx') as writer:  
    df_1.to_excel(writer, sheet_name='Sheet1', index=False)
    df_2.to_excel(writer, sheet_name='Sheet2', index=False)
    df_3.to_excel(writer, sheet_name='Sheet3', index=False)

DataFrame Transformations

Unioning dataframes together with concat

import pandas as pd

df_total = pd.concat([df1,df2,df3])

Replacing null values with zero, blank, previous or preceeding values

import pandas as pd

# replace nulls with zeroes
df['Column with nulls'] = df['Column with nulls'].fillna(0)

# replace nulls with empty string (blank)
df['Column with nulls'] = df['Column with nulls'].fillna('')

# replace nulls with previous non-null value
df['Column with nulls'] = df['Column with nulls'].fillna(method='ffill')

# replace nulls with next non-null value
df['Column with nulls'] = df['Column with nulls'].fillna(method='bfill')

Aggregrating data

Create aggregrated columns grouped by other columns

import pandas as pd

df = df.groupby(['Col1','Col2']).agg(col3_min=('Col3','min'),col3_max=('Col3','max'),col3_sum=('Col3','sum')).reset_index()

Data Clean-up

Rename single column

import pandas as pd

df.rename( columns={'Col1':'Col1_New_Name'}, inplace=True )