Master

enginerring_01_two_quantiative_variables.py

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+#coding:utf-8
+"""
+------------------------------------------------
+@File Name    : enginerring_01_two_quantiative_variables
+@Function     : 
+@Author       : Minux
+@Date         : 2018/9/17
+@Revised Date : 2018/9/17
+------------------------------------------------
+"""
+import numpy as np
+import scipy.stats
+import pandas as pd
+import matplotlib.pyplot as plt
+import re
+import mailbox
+import csv
+
+gapminder = pd.read_csv('gapminder.csv')
+# print(gapminder.info())
+italy = gapminder.query('country == "Italy"')
+# italy.plot.scatter('year','population')
+
+# gapminder.query('country == "India"').plot.scatter('year','population',label='India')
+
+# italy.plot.scatter('year','gdp_per_day',logy=True)
+
+# italy.plot.scatter('gdp_per_day','life_expectancy',logx=True)
+
+size = np.where(italy.year%10==0,32,2)
+data = gapminder.query('(country == "Italy") or (country == "United States")')
+color = np.where(data.country == 'Italy','blue','orange')
+data.plot.scatter('gdp_per_day','life_expectancy',logx=True,c=color,s=size)
+
+plt.legend()
+plt.show()

enginerring_02_graph_visualization.py

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+#coding:utf-8
+"""
+------------------------------------------------
+@File Name    : enginerring_02_graph_visualization
+@Function     : 
+@Author       : Minux
+@Date         : 2018/9/24
+@Revised Date : 2018/9/24
+------------------------------------------------
+"""
+import numpy as np
+import scipy.stats
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib
+from matplotlib import style
+style.use('ggplot')
+import pandas.plotting
+
+from IPython import display
+from ipywidgets import interact,widgets
+
+import re
+import mailbox
+import csv
+
+
+gap_minder = pd.read_csv('gapminder.csv')
+
+def plot_year(year,info_tag=False):
+    data = gap_minder[gap_minder.year==year].sort_values('population', ascending=False)
+
+    if info_tag:
+        population_info()
+
+    color = data.age5_surviving
+    area = 3e-6*data.population
+
+    # customize edge color
+    edgecolor = data.region.map({'Africa':'skyblue','Europe':'gold','America':'palegreen','Asia':'coral'})
+
+    # plt.cla()
+    data.plot.scatter('gdp_per_day','life_expectancy',logx=True,s=area,c=color,
+                      colormap=matplotlib.cm.get_cmap('Purples_r'),vmin=55,vmax=100,linewidths=1,edgecolors=edgecolor,
+                      sharex=False, figsize=(10,7))
+
+    for level in [4, 16, 64]:
+        plt.axvline(level, linestyle=':', color='k')
+
+    plt.axis(xmin=1, xmax=500, ymin=30, ymax=100)
+    plt.title('GDP-LIFE-EXPECTANCY_{}'.format(year))
+    plt.xlabel('$gdp-per-day$')
+    plt.ylabel('$life-expectancy$')
+
+
+def population_info():
+    res = gap_minder[gap_minder.year==2015].groupby('region').population.sum()
+    print(res)
+
+def dynamic_plotting_func():
+    # interact(plot_year, year=range(1960,1970))
+    # population_info()
+    year_list = [1965, 1966, 1967]
+    plt.ion()
+    for _year in year_list:
+        plot_year(_year)
+        plt.pause(2)
+        if _year != year_list[-1]:
+            plt.close()
+    plt.ioff()
+    plt.show()
+
+def plot_matrix_func():
+    gap_minder.set_index('year',inplace=True)
+    gap_minder['log10_gdp_per_day'] = np.log10(gap_minder['gdp_per_day'])
+    data = gap_minder.loc[2015,['log10_gdp_per_day','life_expectancy','age5_surviving','babies_per_woman']]
+    pandas.plotting.scatter_matrix(data, figsize=(9,9))
+    plt.show()
+
+if __name__ == '__main__':
+    plot_matrix_func()
+
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