纯代码分享：Python plotnine数据可视化

深度学习与Python

发布于 2019-06-18 20:50:57

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发布于 2019-06-18 20:50:57

接触过R中的ggplot绘图的伙伴应该被其优雅的绘图所吸引，那么现在大家基本都用python来进行数据处理，在python中也有许多绘图库，除了我们熟悉的matplotlib之外，今天给大家介绍一个拥有ggplot一样绘图美学的python绘图库plotnine。plotnine提供各种不同的可视化，易于适应定制输出。如果你之前接触过R中的ggplot，那么使用plotnine将毫不费力。plotnine安装十分简单，可用pip或者conda直接安装：

# Using pip
$ pip install plotnine         # 1. should be sufficient for most
$ pip install 'plotnine[all]'  # 2. includes extra/optional packages

# Or using conda
$ conda install -c conda-forge plotnine

本次数据集我们使用的是包含有关2,410种美国精酿啤酒的信息数据，大家不需要去下载该数据集，可直接通过下文提供的地址用pandas加载就可以了。

导入库及定义变量

安装好plotnine库之后，在绘图前定义后续使用的一些变量函数。

import pandas as pd
import numpy as np
from plotnine import *
#数据地址
c_remote_data ='https://raw.githubusercontent.com/nickhould/craft-beers-dataset/master/data/processed/beers.csv'
#定义图表颜色
c_col = ["#2f4858", "#f6ae2d", "#f26419",
         "#33658a", "#55dde0", "#2f4858",
         "#2f4858", "#f6ae2d", "#f26419",
         "#33658a", "#55dde0", "#2f4858"]

def labels(from_, to_, step_):
    return pd.Series(np.arange(from_, to_ + step_, step_)).apply(lambda x: '{:,}'.format(x)).tolist()
def breaks(from_, to_, step_):
    return pd.Series(np.arange(from_, to_ + step_, step_)).tolist()

加载数据并索引相应列

这里用pandas直接通过数据地址加载数据，并用filter索引相应的数据列。

data = pd.read_csv(c_remote_data)
data = (
    data.filter([
        'abv',
        'ibu',
        'id',
        'name',
        'style',
        'brewery_id',
        'ounces'
    ]).
    set_index('id')
)

直方图

基本直方图

fig =（
    ggplot（data.dropna（subset = ['abv']））+ 
    geom_histogram（aes（x ='abv'））
）

直方图中添加颜色

fig =(
    ggplot(data.dropna(subset = ['abv']))+ 
    geom_histogram(
        aes(x ='abv'),
        fill = c_col[0],color ='black'))

添加坐标名称

fig = (
    ggplot(data.dropna(subset = ['abv'])) +
    geom_histogram(
        aes(x = 'abv'),
        fill = c_col[0], color = 'black'
    ) +
    labs(
        title ='Distribution of The alcoholic content by volume (abv)',
        x = 'abv - The alcoholic content by volume',
        y = 'Count',))

显示完整坐标刻度

fig = (
    ggplot(data.dropna(subset = ['abv'])) +
    geom_histogram(
        aes(x = 'abv'),
        fill = c_col[0], color = 'black'
    ) +
    labs(
        title ='Distribution of The alcoholic content by volume (abv)',
        x = 'abv - The alcoholic content by volume',
        y = 'Count',
    ) +
    scale_x_continuous(
        limits = (0, 0.14),
        labels = labels(0, 0.14, 0.02),
        breaks = breaks(0, 0.14, 0.02)
    ) +
    scale_y_continuous(
        limits = (0, 350),
        labels = labels(0, 350, 50),
        breaks = breaks(0, 350, 50)))

散点图

fig = (
    ggplot(data.dropna(subset = ['abv'])) +
    geom_point(
        aes(x = 'abv',
            y = 'ibu'),
        fill = c_col[0], color = 'black'
    ) +
    labs(
        title ='Relationship between alcoholic content (abv) and int. bittering untis (ibu)',
        x = 'abv - The alcoholic content by volume',
        y = 'ibu - International bittering units',
    ) +
    scale_x_continuous(
        limits = (0, 0.14),
        labels = labels(0, 0.14, 0.02),
        breaks = breaks(0, 0.14, 0.02)
    )  +
    scale_y_continuous(
        limits = (0, 150),
        labels = labels(0, 150, 30),
        breaks = breaks(0, 150, 30)))

对不同数据点设置不同颜色

data['ounces_str'] = data['ounces']
data['ounces_str'] = data['ounces_str'].apply(str)
fig = (
    ggplot(data.dropna(subset = ['abv'])) +
    geom_point(
        aes(x = 'abv',
            y = 'ibu',
            fill = 'ounces_str'),
        alpha = 0.5,
        color = 'black'
    ) +
    labs(
        title ='Relationship between alcoholic content (abv) and int. bittering untis (ibu)',
        x = 'abv - The alcoholic content by volume',
        y = 'ibu - International bittering units',
    ) +
    scale_fill_manual(
        name = 'Ounces',
        values = c_col) +
    scale_x_continuous(
        limits = (0, 0.14),
        labels = labels(0, 0.14, 0.02),
        breaks = breaks(0, 0.14, 0.02)
    )  +
    scale_y_continuous(
        limits = (0, 150),
        labels = labels(0, 150, 30),
        breaks = breaks(0, 150, 30)))

热图

fig = (
    ggplot(data.dropna(subset = ['abv'])) +
    geom_bin2d(
        aes(x = 'abv',
            y = 'ibu')
    ) +
    labs(
        title ='Relationship between alcoholic content (abv) and int. bittering untis (ibu)',
        x = 'abv - The alcoholic content by volume',
        y = 'ibu - International bittering units',
    ) +
    scale_x_continuous(
        limits = (0, 0.14),
        labels = labels(0, 0.14, 0.02),
        breaks = breaks(0, 0.14, 0.02)
    )  +
    scale_y_continuous(
        limits = (0, 150),
        labels = labels(0, 150, 30),
        breaks = breaks(0, 150, 30)
    ) +
    theme(figure_size = (8, 8)))

箱图

fig = (
    ggplot(data.dropna(subset = ['abv'])) +
    geom_boxplot(
        aes(x = 'ounces_str',
            y = 'abv')
    ) +
    labs(
        title ='Distribution of alcoholic content (abv) by size',
        x = 'size in ounces',
        y = 'abv - The alcoholic content by volume',
    ) +
    scale_y_continuous(
        limits = (0, 0.14),
        labels = labels(0, 0.14, 0.02),
        breaks = breaks(0, 0.14, 0.02)))

花式箱图

fig = (
    ggplot(data.dropna(subset = ['abv'])) +
    geom_violin(
        aes(x = 'ounces_str',
            y = 'abv'),
        fill = c_col[0]
    ) +
    labs(
        title ='Distribution of alcoholic content (abv) by size',
        x = 'size in ounces',
        y = 'abv - The alcoholic content by volume',
    ) +
    scale_y_continuous(
        limits = (0, 0.14),
        labels = labels(0, 0.14, 0.02),
        breaks = breaks(0, 0.14, 0.02)))