Specifying Colors For Multiple Lines On Plot Using Matplotlib And Pandas

Pandas dataframe groupby plot I have a similar dataframe to the one in the above question, but it has around 8 ticker symbols. I've defined a list of colours called 'colors' that c

Solution 1:

• pandas.groupby is not required because you're not aggregating a calculation, such as mean.
• Instead of using .groupby, use seaborn.lineplot with hue='ticker'
  • Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
• Seaborn: Choosing color palettes
  • This plot is using husl
  • Additional options for the husl palette can be found at seaborn.husl_palette

Option 1

• Map colors based on the number of unique 'ticker' values

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pandas_datareader.data as web  # for getting stock data

# get test stock data
tickers = ['msft', 'aapl', 'twtr', 'intc', 'tsm', 'goog', 'amzn', 'fb', 'nvda']
df_list = list()
for ticker in tickers:
    df = web.DataReader(ticker, data_source='yahoo', start='2019-01-31', end='2020-07-21')
    df['ticker'] = ticker
    df_list.append(df)
    
df = pd.concat(df_list).reset_index()

# create color mapping based on all unique values of ticker
ticker = df.ticker.unique()
colors = sns.color_palette('husl', n_colors=len(ticker))  # get a number of colors
cmap = dict(zip(ticker, colors))  # zip values to colors

# plot
plt.figure(figsize=(16, 10))
sns.lineplot(x='Date', y='adj_close', hue='ticker', data=df, palette=cmap)

Option 2

• Use specific colors

colors = ['r', 'b', 'g', 'y', 'orange', 'purple', 'k', 'm', 'w']

plt.figure(figsize=(16, 10))
sns.lineplot(x='Date', y='Adj Close', hue='ticker', data=df, palette=colors)

df.head()

|    | Date                |   High |    Low |   Open |   Close |      Volume |   Adj Close | ticker   |
|---:|:--------------------|-------:|-------:|-------:|--------:|------------:|------------:|:---------|
|  0 | 2019-01-31 00:00:00 | 105.22 | 103.18 | 103.8  |  104.43 | 5.56364e+07 |     102.343 | msft     |
|  1 | 2019-02-01 00:00:00 | 104.1  | 102.35 | 103.78 |  102.78 | 3.55357e+07 |     100.726 | msft     |
|  2 | 2019-02-04 00:00:00 | 105.8  | 102.77 | 102.87 |  105.74 | 3.13151e+07 |     103.627 | msft     |
|  3 | 2019-02-05 00:00:00 | 107.27 | 105.96 | 106.06 |  107.22 | 2.73254e+07 |     105.077 | msft     |
|  4 | 2019-02-06 00:00:00 | 107    | 105.53 | 107    |  106.03 | 2.06098e+07 |     103.911 | msft     |

df.tail()

|      | Date                |   High |    Low |   Open |   Close |      Volume |   Adj Close | ticker   |
|-----:|:--------------------|-------:|-------:|-------:|--------:|------------:|------------:|:---------|
| 3334 | 2020-07-15 00:00:00 | 417.32 | 402.23 | 416.57 |  409.09 | 1.00996e+07 |      409.09 | nvda     |
| 3335 | 2020-07-16 00:00:00 | 408.27 | 395.82 | 400.6  |  405.39 | 8.6241e+06  |      405.39 | nvda     |
| 3336 | 2020-07-17 00:00:00 | 409.94 | 403.51 | 409.02 |  408.06 | 6.6571e+06  |      408.06 | nvda     |
| 3337 | 2020-07-20 00:00:00 | 421.25 | 406.27 | 410.97 |  420.43 | 7.1213e+06  |      420.43 | nvda     |
| 3338 | 2020-07-21 00:00:00 | 422.4  | 411.47 | 420.52 |  413.14 | 6.9417e+06  |      413.14 | nvda     |