使用matplotlib和pandas为绘图上的多条线指定颜色 [英] Specifying colors for multiple lines on plot using matplotlib and pandas

问题描述

选项2

• 使用特定颜色

  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()

  ||日期|高|低|开启|关闭|数量调整关闭|股票|| ---:|:-------------------- | -------:| -------:| -------:| --------:| ------------:| ------------:|:--------- ||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()

  ||日期|高|低|开启|关闭|数量调整关闭|股票|| -----:|:-------------------- | -------:| -------:|------:| --------:| ------------:| ------------:|:--------- ||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 | 

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 correspond with the tickers, but when I do:

df.groupby('ticker')['adj_close'].plot(color=colors)

all the lines on the plot for each of the tickers are the same colour (i.e. the first colour in the list 'colors').

I was wondering how I can specify the line for each ticker to be in its corresponding colour from the list 'colors'?

Thanks in advance!

解决方案

• 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     |

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