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... pandas pipe.


Pandas code can get quite nasty inside of your jupyter notebook. It's not just the syntax, it's the infinite amount of scrolling too. In this series of videos we're going to explore a way to clean this up. This series of videos is inspired by the modern pandas blogposts originally written by Tom Augspurger.


Notes

The final plot is created via;

import pandas as pd 

df = pd.read_csv('https://calmcode.io/datasets/bigmac.csv')

def set_dtypes(dataf):
    return (dataf
            .assign(date=lambda d: pd.to_datetime(d['date']))
            .sort_values(['currency_code', 'date']))

def remove_outliers(dataf, min_row_country=32):
    countries = (dataf
                .groupby('currency_code')
                .agg(n=('name', 'count'))
                .loc[lambda d: d['n'] >= min_row_country]
                .index)
    return (dataf
            .loc[lambda d: d['currency_code'].isin(countries)])

(df
  .pipe(set_dtypes)
  .pipe(remove_outliers, min_row_country=32))

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