Pandas dataframe groupby plot

Simple plot,

you can use:

df.plot(x='Date',y='adj_close')

Or you can set the index to be Date beforehand, then it's easy to plot the column you want:

df.set_index('Date', inplace=True)
df['adj_close'].plot()

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If you want a chart with one series by ticker on it

You need to groupby before:

df.set_index('Date', inplace=True)
df.groupby('ticker')['adj_close'].plot(legend=True)

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If you want a chart with individual subplots:

grouped = df.groupby('ticker')

ncols=2
nrows = int(np.ceil(grouped.ngroups/ncols))

fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(12,4), sharey=True)

for (key, ax) in zip(grouped.groups.keys(), axes.flatten()):
    grouped.get_group(key).plot(ax=ax)

ax.legend()
plt.show()

Similar to Julien's answer above, I had success with the following:

fig, ax = plt.subplots(figsize=(10,4))
for key, grp in df.groupby(['ticker']):
    ax.plot(grp['Date'], grp['adj_close'], label=key)

ax.legend()
plt.show()

This solution might be more relevant if you want more control in matlab.

Solution inspired by: https://stackoverflow.com/a/52526454/10521959

• The question is How can I plot based on the ticker the adj_close versus Date?
  • This can be accomplished by reshaping the dataframe to a wide format with .pivot or .groupby, or by plotting the existing long form dataframe directly with seaborn.
• In the following sample data, the 'Date' column has a datetime64[ns] Dtype.
  • Convert the Dtype with pandas.to_datetime if needed.
• Tested in python 3.10, pandas 1.4.2, matplotlib 3.5.1, seaborn 0.11.2

Imports and Sample Data

import pandas as pd
import pandas_datareader as web  # for sample data; this can be installed with conda if using Anaconda, otherwise pip
import seaborn as sns
import matplotlib.pyplot as plt

# sample stock data, where .iloc[:, [5, 6]] selects only the 'Adj Close' and 'tkr' column
tickers = ['aapl', 'acn']
df = pd.concat((web.DataReader(ticker, data_source='yahoo', start='2020-01-01', end='2022-06-21')
                .assign(ticker=ticker) for ticker in tickers)).iloc[:, [5, 6]]

# display(df.head())
        Date  Adj Close ticker
0 2020-01-02  73.785904   aapl
1 2020-01-03  73.068573   aapl
2 2020-01-06  73.650795   aapl
3 2020-01-07  73.304420   aapl
4 2020-01-08  74.483604   aapl

# display(df.tail())
           Date   Adj Close ticker
1239 2022-06-14  275.119995    acn
1240 2022-06-15  281.190002    acn
1241 2022-06-16  270.899994    acn
1242 2022-06-17  275.380005    acn
1243 2022-06-21  282.730011    acn

pandas.DataFrame.pivot & pandas.DataFrame.plot

• pandas plots with matplotlib as the default backend.
• Reshaping the dataframe with pandas.DataFrame.pivot converts from long to wide form, and puts the dataframe into the correct format to plot.
• .pivot does not aggregate data, so if there is more than 1 observation per index, per ticker, then use .pivot_table
• Adding subplots=True will produce a figure with two subplots.

# reshape the long form data into a wide form
dfp = df.pivot(index='Date', columns='ticker', values='Adj Close')

# display(dfp.head())
ticker           aapl         acn
Date                             
2020-01-02  73.785904  203.171112
2020-01-03  73.068573  202.832764
2020-01-06  73.650795  201.508224
2020-01-07  73.304420  197.157654
2020-01-08  74.483604  197.544434

# plot
ax = dfp.plot(figsize=(11, 6))

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• Use seaborn, which accepts long form data, so reshaping the dataframe to a wide form isn't necessary.
• seaborn is a high-level api for matplotlib

sns.lineplot: axes-level plot

fig, ax = plt.subplots(figsize=(11, 6))
sns.lineplot(data=df, x='Date', y='Adj Close', hue='ticker', ax=ax)

sns.relplot: figure-level plot

• Adding row='ticker', or col='ticker', will generate a figure with two subplots.

g = sns.relplot(kind='line', data=df, x='Date', y='Adj Close', hue='ticker', aspect=1.75)