How to generate 2-yaxis graphs on a panel data per id?

I have a dataset, df that looks like this:

Date	Code	City	State	Quantity x	Quantity y	Population	Cases	Deaths
2019-01	10001	Los Angeles	CA		445		0	0
2019-01	10002	Sacramento	CA	4450	556		0	0
2020-03	12223	Houston	TX	440	4440	35000000	23	11
…	…	…	…	…	…	…	…	…
2021-07	10002	Sacramento	CA	3220	NA	5444000	211	22

My start and end date are the same for all cities. I have over 4000 different cities, and would like to plot a 2-yaxis graph for each city, using something similar to the following code:

import matplotlib.pyplot as plt

fig, ax1 = plt.subplots(figsize=(9,9))

color = 'tab:red'
ax1.set_xlabel('Date')
ax1.set_ylabel('Quantity X', color=color)
ax1.plot(df['Quantity x'], color=color)
ax1.tick_params(axis='y', labelcolor=color)

ax2 = ax1.twinx()
color2 = 'tab:blue'
ax2.set_ylabel('Deaths', color=color2)
ax2.plot(df['Deaths'], color=color2)
ax2.tick_params(axis='y', labelcolor=color2)
plt.show()

I would like to create a loop so that the code above runs for every Code that is related to a City, with quantity x and deaths, and it saves each graph made into a folder. How can I create a loop that does that, and stops every different Code?

Observations: Some values on df['Quantity x] and df[Population] are left blank.

Answer

If I understood you correctly, you are looking for a filtering functionality:

import matplotlib.pyplot as plt
import pandas as pd


def plot_quantity_and_death(df):
    # your code
    fig, ax1 = plt.subplots(figsize=(9, 9))

    color = 'tab:red'
    ax1.set_xlabel('Date')
    ax1.set_ylabel('Quantity X', color=color)
    ax1.plot(df['Quantity x'], color=color)
    ax1.tick_params(axis='y', labelcolor=color)

    ax2 = ax1.twinx()
    color2 = 'tab:blue'
    ax2.set_ylabel('Deaths', color=color2)
    ax2.plot(df['Deaths'], color=color2)
    ax2.tick_params(axis='y', labelcolor=color2)

    # save & close addon
    plt.savefig(f"Code_{str(df['Code'].iloc[0])}.png")
    plt.close()


df = pd.DataFrame()  # this needs to be replaced by your dataset

# get unique city codes, loop over them, filter data and plot it
unique_codes = pd.unique(df['Code'])
for code in unique_codes:
    filtered_df = df[df['Code'] == code]
    plot_quantity_and_death(filtered_df)