1. Plot Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | #import matplotlib.pyplot as plt #plt.show() #plt.savefig('a.png') def line_plot(): from numpy import sin from matplotlib import pyplot x = [x*0.1 for x in range(100)] y = sin(x) pyplot.plot(x, y) pyplot.show() def bar_chart(): from random import seed from random import randint from matplotlib import pyplot seed(1) x = ['red', 'green', 'blue'] y = [randint(0, 100), randint(0, 100), randint(0, 100)] pyplot.bar(x, y) pyplot.show() def histogram_plot(): from numpy.random import seed from numpy.random import randn from matplotlib import pyplot seed(1) x = randn(1000) pyplot.hist(x) #, bins=100) pyplot.show() def scatter_plot(): from numpy.random import seed from numpy.random import randn from matplotlib import pyplot seed(1) x = 20 * randn(1000) + 100 y = x + (10 * randn(1000) + 50) pyplot.scatter(x, y) pyplot.show() def box_whisker_plot(): from numpy.random import seed from numpy.random import randn from matplotlib import pyplot seed(1) x = [randn(1000), 5 * randn(1000), 10 * randn(1000)] pyplot.boxplot(x) pyplot.show() def pandas_plot(): import matplotlib.pyplot as plt import pandas url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] data = pandas.read_csv(url, names=names) data.hist() plt.show() data.plot(kind='density', subplots=True, layout=(3,3), sharex=False) plt.show() data.plot(kind='box', subplots=True, layout=(3,3), sharex=False, sharey=False) plt.show() data1 = data[data.columns[2:4]] data1.plot() plt.show() data1.plot(kind='density') plt.show() data1 = data.loc[:,'pres':'mass'] data1.plot(kind='density') plt.show() def pandas_random(): import pandas as pd import numpy as np np.random.seed(5) df = pd.DataFrame(np.random.randint(100, size=(100, 6)), columns=list('ABCDEF'), index=['R{}'.format(i) for i in range(100)]) print(df) print(df.head()) print(df[['C','E']].head()) print(df[df.columns[2:4]].head()) print(df.loc[:,df.columns.isin(list('ACE'))].head()) print(df.loc['R6':'R10','C':'E']) print(df.iloc[2:6,2:5]) print(df.iloc[0:6,0:2].copy()) print(pd.DataFrame(df,columns=['B','E']).head()) print(df.drop(['A','C'],axis=1).head()) def time_series_line_plot(): from pandas import Series from matplotlib import pyplot series = Series.from_csv('daily-minimum-temperatures-in-me.csv', header=0) print(series.head()) #series = series.astype(float) series.plot() pyplot.show() def time_series_group_line_plot(): from pandas import DataFrame from pandas import TimeGrouper from pandas import Series from matplotlib import pyplot series = Series.from_csv('daily-minimum-temperatures-in-me.csv', header=0) groups = series.groupby(TimeGrouper('A')) years = DataFrame() for name, group in groups: years[name.year] = group.values years.plot(subplots=True, legend=False) pyplot.show() if __name__ == "__main__": #line_plot() #bar_chart() #histogram_plot() #scatter_plot() #box_whisker_plot() #time_series_line_plot() #time_series_group_line_plot() #pandas_plot() pandas_random() |
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