Matplotlib中按列值的颜色

Question

ggplot2在R 中使用库我最喜欢的一个方面是能够轻松指定美学.我可以快速制作一个散点图并应用与特定列关联的颜色,我希望能够使用python/pandas/matplotlib执行此操作.我想知道是否有任何便利功能,人们用它来使用pandas数据帧和Matplotlib将颜色映射到值？

##ggplot scatterplot example with R dataframe, `df`, colored by col3
ggplot(data = df, aes(x=col1, y=col2, color=col3)) + geom_point()

##ideal situation with pandas dataframe, 'df', where colors are chosen by col3
df.plot(x=col1,y=col2,color=col3)

编辑:感谢您的回复,但我想包含一个示例数据框,以澄清我的要求.两列包含数字数据,第三列是分类变量.我想的脚本将根据此值指定颜色.

import pandas as pd
df = pd.DataFrame({'Height':np.random.normal(10),
                   'Weight':np.random.normal(10),
                   'Gender': ["Male","Male","Male","Male","Male",
                              "Female","Female","Female","Female","Female"]})

Answer 1

2015年10月更新

Seaborn精彩地处理这个用例:

import numpy 
import pandas
from  matplotlib import pyplot
import seaborn
seaborn.set(style='ticks')

numpy.random.seed(0)
N = 37
_genders= ['Female', 'Male', 'Non-binary', 'No Response']
df = pandas.DataFrame({
    'Height (cm)': numpy.random.uniform(low=130, high=200, size=N),
    'Weight (kg)': numpy.random.uniform(low=30, high=100, size=N),
    'Gender': numpy.random.choice(_genders, size=N)
})

fg = seaborn.FacetGrid(data=df, hue='Gender', hue_order=_genders, aspect=1.61)
fg.map(pyplot.scatter, 'Weight (kg)', 'Height (cm)').add_legend()

哪个立即输出:

老答案

在这种情况下,我会直接使用matplotlib.

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

def dfScatter(df, xcol='Height', ycol='Weight', catcol='Gender'):
    fig, ax = plt.subplots()
    categories = np.unique(df[catcol])
    colors = np.linspace(0, 1, len(categories))
    colordict = dict(zip(categories, colors))  

    df["Color"] = df[catcol].apply(lambda x: colordict[x])
    ax.scatter(df[xcol], df[ycol], c=df.Color)
    return fig

if 1:
    df = pd.DataFrame({'Height':np.random.normal(size=10),
                       'Weight':np.random.normal(size=10),
                       'Gender': ["Male","Male","Unknown","Male","Male",
                                  "Female","Did not respond","Unknown","Female","Female"]})    
    fig = dfScatter(df)
    fig.savefig('fig1.png')

这给了我:

据我所知,该颜色列可以是任何matplotlib兼容颜色(RBGA元组,HTML名称,十六进制值等).

我无法获得除数字地图以外的任何数字值.

Answer 2

实际上你可以使用ggplot for python:

from ggplot import *
import numpy as np
import pandas as pd

df = pd.DataFrame({'Height':np.random.randn(10),
                   'Weight':np.random.randn(10),
                   'Gender': ["Male","Male","Male","Male","Male",
                              "Female","Female","Female","Female","Female"]})


ggplot(aes(x='Height', y='Weight', color='Gender'), data=df)  + geom_point()

Answer 3

https://seaborn.pydata.org/generated/seaborn.scatterplot.html

import numpy 
import pandas
import seaborn as sns

numpy.random.seed(0)
N = 37
_genders= ['Female', 'Male', 'Non-binary', 'No Response']
df = pandas.DataFrame({
    'Height (cm)': numpy.random.uniform(low=130, high=200, size=N),
    'Weight (kg)': numpy.random.uniform(low=30, high=100, size=N),
    'Gender': numpy.random.choice(_genders, size=N)
})

sns.scatterplot(data=df, x='Height (cm)', y='Weight (kg)', hue='Gender')

Answer 4

您可以使用plot 方法的color参数来定义每列所需的颜色。例如：

from pandas import DataFrame
data = DataFrame({'a':range(5),'b':range(1,6),'c':range(2,7)})
colors = ['yellowgreen','cyan','magenta']
data.plot(color=colors)

您可以使用颜色名称或颜色十六进制代码，例如 '#000000' 表示黑色。您可以在 matplotlib 的 color.py 文件中找到所有定义的颜色名称。下面是 matplotlib 的 github 存储库中 color.py 文件的链接。

https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/colors.py

Answer 5

• The OP is coloring by a categorical column, but this answer is for coloring by a column that is numeric, or can be interpreted as numeric, such as a datetime dtype.
• pandas.DataFrame.plot and matplotlib.pyplot.scatter can take a c or color parameter, which must be a color, a sequence of colors, or a sequence of numbers.
• Tested in python 3.8, pandas 1.3.1, and matplotlib 3.4.2
• Choosing Colormaps in Matplotlib for other valid cmap options.

Imports and Test Data

• 'Date' is already a datetime64[ns] dtype from DataReader
• conda install -c anaconda pandas-datareader or pip install pandas-datareader depending on your environment.

import pandas as pd
import matplotlib.pyplot as plt
import pandas_datareader as web  # for data; not part of pandas

tickers = 'amzn'
df = web.DataReader(ticker, data_source='yahoo', start='2018-01-01', end='2021-01-01').reset_index()
df['ticker'] = ticker

        Date        High          Low         Open        Close   Volume    Adj Close ticker
0 2018-01-02  1190.00000  1170.510010  1172.000000  1189.010010  2694500  1189.010010   amzn
1 2018-01-03  1205.48999  1188.300049  1188.300049  1204.199951  3108800  1204.199951   amzn

c as a number

pandas.DataFrame.plot

• df.Date.dt.month creates a pandas.Series of month numbers

ax = df.plot(kind='scatter', x='Date', y='High', c=df.Date.dt.month, cmap='Set3', figsize=(11, 4), title='c parameter as a month number')
plt.show()

matplotlib.pyplot.scatter

fig, ax = plt.subplots(figsize=(11, 4))
ax.scatter(data=df, x='Date', y='High', c=df.Date.dt.month, cmap='Set3')
ax.set(title='c parameter as a month number', xlabel='Date', ylabel='High')
plt.show()

c as a datetime dtype

pandas.DataFrame.plot

ax = df.plot(kind='scatter', x='Date', y='High', c='Date', cmap='winter', figsize=(11, 4), title='c parameter as a datetime dtype')
plt.show()

matplotlib.pyplot.scatter

fig, ax = plt.subplots(figsize=(11, 4))
ax.scatter(data=df, x='Date', y='High', c='Date', cmap='winter')
ax.set(title='c parameter as a datetime dtype', xlabel='Date', ylabel='High')
plt.show()