Python利用matplotlib画出漂亮的分析图表
目录
- 前言
- 数据集引入
- 折线图
- 饼图
- 散点图
- 面积图
- 直方图
- 条形图
前言
作为一名优秀的分析师,还是得学会一些让图表漂亮的技巧,这样子拿出去才更加有面子哈哈。好了,今天的锦囊就是介绍一下各种常见的图表,可以怎么来画吧。
数据集引入
首先引入数据集,我们还用一样的数据集吧,分别是 Salary_Ranges_by_Job_Classification
以及 GlobalLandTemperaturesByCity
。(具体数据集可以后台回复 plot
获取)
# 导入一些常用包 import pandas as pd import numpy as np import seaborn as sns %matplotlib inline import matplotlib.pyplot as plt import matplotlib as mpl plt.style.use('fivethirtyeight') #解决中文显示问题,Mac from matplotlib.font_manager import FontProperties # 查看本机plt的有效style print(plt.style.available) # 根据本机available的style,选择其中一个,因为之前知道ggplot很好看,所以我选择了它 mpl.style.use(['ggplot']) # ['_classic_test', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2'] # 数据集导入 # 引入第 1 个数据集 Salary_Ranges_by_Job_Classification salary_ranges = pd.read_csv('./data/Salary_Ranges_by_Job_Classification.csv') # 引入第 2 个数据集 GlobalLandTemperaturesByCity climate = pd.read_csv('./data/GlobalLandTemperaturesByCity.csv') # 移除缺失值 climate.dropna(axis=0, inplace=True) # 只看中国 # 日期转换, 将dt 转换为日期,取年份, 注意map的用法 climate['dt'] = pd.to_datetime(climate['dt']) climate['year'] = climate['dt'].map(lambda value: value.year) climate_sub_china = climate.loc[climate['Country'] == 'China'] climate_sub_china['Century'] = climate_sub_china['year'].map(lambda x:int(x/100 +1)) climate.head()
折线图
折线图是比较简单的图表了,也没有什么好优化的,颜色看起来顺眼就好了。下面是从网上找到了颜色表,可以从中挑选~
# 选择上海部分天气数据 df1 = climate.loc[(climate['Country']=='China')&(climate['City']=='Shanghai')&(climate['dt']>='2010-01-01')]\ .loc[:,['dt','AverageTemperature']]\ .set_index('dt') df1.head()
# 折线图 df1.plot(colors=['lime']) plt.title('AverageTemperature Of ShangHai') plt.ylabel('Number of immigrants') plt.xlabel('Years') plt.show()
上面这是单条折线图,多条折线图也是可以画的,只需要多增加几列。
# 多条折线图 df1 = climate.loc[(climate['Country']=='China')&(climate['City']=='Shanghai')&(climate['dt']>='2010-01-01')]\ .loc[:,['dt','AverageTemperature']]\ .rename(columns={'AverageTemperature':'SH'}) df2 = climate.loc[(climate['Country']=='China')&(climate['City']=='Tianjin')&(climate['dt']>='2010-01-01')]\ .loc[:,['dt','AverageTemperature']]\ .rename(columns={'AverageTemperature':'TJ'}) df3 = climate.loc[(climate['Country']=='China')&(climate['City']=='Shenyang')&(climate['dt']>='2010-01-01')]\ .loc[:,['dt','AverageTemperature']]\ .rename(columns={'AverageTemperature':'SY'}) # 合并 df123 = df1.merge(df2, how='inner', on=['dt'])\ .merge(df3, how='inner', on=['dt'])\ .set_index(['dt']) df123.head()
# 多条折线图 df123.plot() plt.title('AverageTemperature Of 3 City') plt.ylabel('Number of immigrants') plt.xlabel('Years') plt.show()
饼图
接下来是画饼图,我们可以优化的点多了一些,比如说从饼块的分离程度,我们先画一个“低配版”的饼图。
df1 = salary_ranges.groupby('SetID', axis=0).sum()
# “低配版”饼图 df1['Step'].plot(kind='pie', figsize=(7,7), autopct='%1.1f%%', shadow=True) plt.axis('equal') plt.show()
# “高配版”饼图 colors = ['lightgreen', 'lightblue'] #控制饼图颜色 ['lightgreen', 'lightblue', 'pink', 'purple', 'grey', 'gold'] explode=[0, 0.2] #控制饼图分离状态,越大越分离 df1['Step'].plot(kind='pie', figsize=(7, 7), autopct = '%1.1f%%', startangle=90, shadow=True, labels=None, pctdistance=1.12, colors=colors, explode = explode) plt.axis('equal') plt.legend(labels=df1.index, loc='upper right', fontsize=14) plt.show()
散点图
散点图可以优化的地方比较少了,ggplot2的配色都蛮好看的,正所谓style选的好,省很多功夫!
# 选择上海部分天气数据 df1 = climate.loc[(climate['Country']=='China')&(climate['City']=='Shanghai')&(climate['dt']>='2010-01-01')]\ .loc[:,['dt','AverageTemperature']]\ .rename(columns={'AverageTemperature':'SH'}) df2 = climate.loc[(climate['Country']=='China')&(climate['City']=='Shenyang')&(climate['dt']>='2010-01-01')]\ .loc[:,['dt','AverageTemperature']]\ .rename(columns={'AverageTemperature':'SY'}) # 合并 df12 = df1.merge(df2, how='inner', on=['dt']) df12.head()
# 散点图 df12.plot(kind='scatter', x='SH', y='SY', figsize=(10, 6), color='darkred') plt.title('Average Temperature Between ShangHai - ShenYang') plt.xlabel('ShangHai') plt.ylabel('ShenYang') plt.show()
面积图
# 多条折线图 df1 = climate.loc[(climate['Country']=='China')&(climate['City']=='Shanghai')&(climate['dt']>='2010-01-01')]\ .loc[:,['dt','AverageTemperature']]\ .rename(columns={'AverageTemperature':'SH'}) df2 = climate.loc[(climate['Country']=='China')&(climate['City']=='Tianjin')&(climate['dt']>='2010-01-01')]\ .loc[:,['dt','AverageTemperature']]\ .rename(columns={'AverageTemperature':'TJ'}) df3 = climate.loc[(climate['Country']=='China')&(climate['City']=='Shenyang')&(climate['dt']>='2010-01-01')]\ .loc[:,['dt','AverageTemperature']]\ .rename(columns={'AverageTemperature':'SY'}) # 合并 df123 = df1.merge(df2, how='inner', on=['dt'])\ .merge(df3, how='inner', on=['dt'])\ .set_index(['dt']) df123.head()
colors = ['red', 'pink', 'blue'] #控制饼图颜色 ['lightgreen', 'lightblue', 'pink', 'purple', 'grey', 'gold'] df123.plot(kind='area', stacked=False, figsize=(20, 10), colors=colors) plt.title('AverageTemperature Of 3 City') plt.ylabel('AverageTemperature') plt.xlabel('Years') plt.show()
直方图
# 选择上海部分天气数据 df = climate.loc[(climate['Country']=='China')&(climate['City']=='Shanghai')&(climate['dt']>='2010-01-01')]\ .loc[:,['dt','AverageTemperature']]\ .set_index('dt') df.head()
# 最简单的直方图 df['AverageTemperature'].plot(kind='hist', figsize=(8,5), colors=['grey']) plt.title('ShangHai AverageTemperature Of 2010-2013') # add a title to the histogram plt.ylabel('Number of month') # add y-label plt.xlabel('AverageTemperature') # add x-label plt.show()
条形图
# 选择上海部分天气数据 df = climate.loc[(climate['Country']=='China')&(climate['City']=='Shanghai')&(climate['dt']>='2010-01-01')]\ .loc[:,['dt','AverageTemperature']]\ .set_index('dt') df.head()
df.plot(kind='bar', figsize = (10, 6)) plt.xlabel('Month') plt.ylabel('AverageTemperature') plt.title('AverageTemperature of shanghai') plt.show()
df.plot(kind='barh', figsize=(12, 16), color='steelblue') plt.xlabel('AverageTemperature') plt.ylabel('Month') plt.title('AverageTemperature of shanghai') plt.show()
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