[FIXED] How to display a history chart for multiple symbols with yahoo query?

Issue

What is the command to display the chart in https://yahooquery.dpguthrie.com/guide/ticker/historical/

I tried this code

import pandas as pd
import matplotlib.pyplot as plt
from yahooquery import Ticker

tickers = Ticker('aapl nflx', asynchronous=True)
df = tickers.history()
df["adjclose"].plot()
plt.xticks(rotation=90)
plt.show()

But it is just showing one series in the chart, like this:

How can I create aapl and nflx as two series on the one chart?

Solution

First, get your df into a shape that is easier to digest for plotting methods, using df.pivot:

import pandas as pd
from yahooquery import Ticker

tickers = Ticker('aapl nflx', asynchronous=True)
df = tickers.history()

df_pivot = df.reset_index().pivot(index='date',
                                  columns='symbol', values='adjclose')

symbol            aapl        nflx
date                              
2022-01-03  181.259918  597.369995
2022-01-04  178.959457  591.150024
2022-01-05  174.199142  567.520020
2022-01-06  171.291199  553.289978
2022-01-07  171.460495  541.059998

Next, have a look at sns.lineplot:

import matplotlib.pyplot as plt
import matplotlib.dates as md
import matplotlib.ticker as mtick

import seaborn as sns
sns.set_theme()

fig, ax = plt.subplots(figsize=(10,6))

ax = sns.lineplot(data=df_pivot, palette=['r','b'], dashes=False)

# adjust axes for readability
ax.xaxis.set_major_locator(md.WeekdayLocator(byweekday = 1))
ax.set_xlim(df_pivot.index.min(), df_pivot.index.max())
ax.yaxis.set_major_locator(mtick.MultipleLocator(50))

plt.xticks(rotation=90)
plt.tight_layout()
plt.show()

Result:

Incidentally, if you want to compare both stocks, it may make more sense to plot the percentages. E.g.:

df_pivot_perc = df_pivot.div(df_pivot.iloc[0,:]).mul(100)
fig, ax = plt.subplots(figsize=(10,6))

ax = sns.lineplot(data=df_pivot_perc, palette=['r','b'], dashes=False)
ax.xaxis.set_major_locator(md.WeekdayLocator(byweekday = 1))
ax.set_xlim(df_pivot.index.min(), df_pivot.index.max())

fmt = '%.0f%%'
yticks = mtick.FormatStrFormatter(fmt)
ax.yaxis.set_major_formatter(yticks)
ax.yaxis.set_major_locator(mtick.MultipleLocator(10))

plt.xticks(rotation=90)
plt.tight_layout()
plt.show()

Result:

Answered By - ouroboros1