Pandas' EMA not matching the stock's EMA?

Question

I am trying to use Python (with Pandas) to calculate the 20-day Exponential Moving Averages (EMA) of daily stock data for Intel (INTC). Pandas has a number of ways of doing this

Answer 1

Sort the DataFrame so that the dates are in increasing order. Since your data is in decreasing order by date, if you don't sort the dates first, your ewm calculation exponentially weights the earliest dates the most, rather than the latest date (as it should be).

import pandas as pd

df = pd.read_csv('intc_data.txt', parse_dates=['Date'], index_col=['Date'])
df['backward_ewm'] = df['Close'].ewm(span=20,min_periods=0,adjust=False,ignore_na=False).mean()
df = df.sort_index()
df['ewm'] = df['Close'].ewm(span=20,min_periods=0,adjust=False,ignore_na=False).mean()
print(df[['ewm', 'backward_ewm']].tail())

yields

                  ewm  backward_ewm
Date                               
2018-01-26  45.370936     48.205638
2018-01-29  45.809895     48.008337
2018-01-30  46.093714     47.800794
2018-01-31  46.288599     47.696667
2018-02-01  46.418256     47.650000

This agrees with Marketwatch which says the EWMA(20) on 2018-02-01 was 46.42.