機器學(xué)習(xí)記錄

Matplotlib

引入庫

import matplotlib.pyplot as plt import numpy as np import pandas as pd # data from: https://github.com/KeithGalli/matplotlib_tutorial data_dir = "/data_dir"

.plot()

x = [0, 1, 2, 3, 4, 5] y = [0, 2, 4, 6, 8, 10]# 設(shè)置圖片大小 plt.figure(figsize=(5, 3), dpi=300)# plt.plot(x, y, # label='label legend', # color='red', # linewidth=2, # marker='x', # markersize=8, # markeredgecolor='blue', # linestyle='--' # ) plt.plot(x, y,'r^-.',label='2x')x2 = np.arange(0, 5, 0.5) plt.plot(x2, x2**2, 'b', label='X^2') plt.plot(x2[:6], x2[:6]**3, 'g', label='X^3') plt.plot(x2[5:], x2[5:]**3, 'y--', label='X^3')plt.title('First Graph!', fontdict={'fontsize': 20})# 設(shè)置坐標軸 plt.xlabel('X Axis') plt.ylabel('Y Axis')# 設(shè)置顯示的坐標 # plt.xticks([0, 1, 2, 5]) # plt.yticks([0, 1, 2, 10, 20])# 說明 plt.legend()# plt.savefig('./python-matplotlib.001.png', dpi=600)plt.show()

.bar()

labels = ['A', 'B', 'C'] values = [1, 4, 2]# 這個要放在前面 plt.figure(figsize=(6, 2), dpi=300)bars = plt.bar(labels, values) bars[0].set_hatch('/') bars[1].set_hatch('.') bars[2].set_hatch('o')plt.show()

labels = ['A', 'B', 'C'] values = [1, 4, 2]# 這個要放在前面 plt.figure(figsize=(6, 2), dpi=300)bars = plt.bar(labels, values)patterns = ['/', 'O', '*'] for bar in bars:bar.set_hatch(patterns.pop(0))plt.show()

df = pd.read_csv(f'{data_dir}/matplotlib_tutorial/gas_prices.csv') df = df.sort_values('Year') print(df.head())plt.figure(figsize=(8, 5))# 只展示以下國家的數(shù)據(jù) countries_to_look_at = ['Australia', 'USA']for country in df: # if country != 'Year':if country in countries_to_look_at:plt.plot(df.Year, df[country], marker='.', label=country)plt.title('Gass Prices over time (in USD)', fontdict={'fontsize': 18, 'fontweight': 'bold'}) plt.xlabel('Year') plt.ylabel('US Dollars')plt.xticks(df.Year[::3].tolist() + [2011])plt.legend()# plt.savefig('./python-matplotlib.002.png', dpi=300)plt.show() Year Australia Canada France Germany Italy Japan Mexico \ 0 1990 NaN 1.87 3.63 2.65 4.59 3.16 1.00 1 1991 1.96 1.92 3.45 2.90 4.50 3.46 1.30 2 1992 1.89 1.73 3.56 3.27 4.53 3.58 1.50 3 1993 1.73 1.57 3.41 3.07 3.68 4.16 1.56 4 1994 1.84 1.45 3.59 3.52 3.70 4.36 1.48 South Korea UK USA 0 2.05 2.82 1.16 1 2.49 3.01 1.14 2 2.65 3.06 1.13 3 2.88 2.84 1.11 4 2.87 2.99 1.11

df = pd.read_csv(f'{data_dir}/matplotlib_tutorial/fifa_data.csv') print(df.head()) Unnamed: 0 ID Name Age \ 0 0 158023 L. Messi 31 1 1 20801 Cristiano Ronaldo 33 2 2 190871 Neymar Jr 26 3 3 193080 De Gea 27 4 4 192985 K. De Bruyne 27 Photo Nationality \ 0 https://cdn.sofifa.org/players/4/19/158023.png Argentina 1 https://cdn.sofifa.org/players/4/19/20801.png Portugal 2 https://cdn.sofifa.org/players/4/19/190871.png Brazil 3 https://cdn.sofifa.org/players/4/19/193080.png Spain 4 https://cdn.sofifa.org/players/4/19/192985.png Belgium Flag Overall Potential \ 0 https://cdn.sofifa.org/flags/52.png 94 94 1 https://cdn.sofifa.org/flags/38.png 94 94 2 https://cdn.sofifa.org/flags/54.png 92 93 3 https://cdn.sofifa.org/flags/45.png 91 93 4 https://cdn.sofifa.org/flags/7.png 91 92 Club ... Composure Marking StandingTackle SlidingTackle \ 0 FC Barcelona ... 96.0 33.0 28.0 26.0 1 Juventus ... 95.0 28.0 31.0 23.0 2 Paris Saint-Germain ... 94.0 27.0 24.0 33.0 3 Manchester United ... 68.0 15.0 21.0 13.0 4 Manchester City ... 88.0 68.0 58.0 51.0 GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause 0 6.0 11.0 15.0 14.0 8.0 €226.5M 1 7.0 11.0 15.0 14.0 11.0 €127.1M 2 9.0 9.0 15.0 15.0 11.0 €228.1M 3 90.0 85.0 87.0 88.0 94.0 €138.6M 4 15.0 13.0 5.0 10.0 13.0 €196.4M [5 rows x 89 columns] bins = np.arange(40, 100, 10) plt.hist(df.Overall) plt.hist(df.Overall, bins=bins, color='#0abcde')plt.xticks(bins)plt.ylabel('Number of Players') plt.xlabel('Skill Level') plt.title('Distribution of Player Skills in FIFA 2018')plt.show()

left = df.loc[df['Preferred Foot'] == 'Left'].count()[0] right = df.loc[df['Preferred Foot'] == 'Right'].count()[0] print(left) print(right)labels = ['Left', 'Right'] colors = ['#ababab', '#cdcdcd']plt.pie([left, right],labels=labels,colors=colors,autopct='%.2f' )plt.title('Foot Preference of FIFA Players')plt.show() 4211 13948

df.Weight = [int(x.strip('lbs')) if type(x) == str else x for x in df.Weight]light = df.loc[df.Weight < 125].count()[0] light_medium = df.loc[(df.Weight >= 125) & (df.Weight < 150)].count()[0] medium = df.loc[(df.Weight >= 150) & (df.Weight < 175)].count()[0] medium_heavy = df.loc[(df.Weight >= 175) & (df.Weight < 200)].count()[0] heavy = df.loc[df.Weight >= 200].count()[0]# plt.style.use('default') plt.style.use('ggplot')weights = [light, light_medium, medium, medium_heavy, heavy] labels = ['Under 125', '125-150', '150-175', '175-200', 'Over 200'] explode = (.4, .2, 0, 0, .4)plt.title('Weight Distribution of FIFA Players (in lbs)')# pctdistance 百分比顯示的位置離圓心的距離, 半徑的 百分比 # explode 塊是否有空白間隙 plt.pie(weights,labels=labels,autopct='%.2f',pctdistance=.8,explode=explode )plt.show()

barcelona = df.loc[df.Club == 'FC Barcelona']['Overall'] madrid = df.loc[df.Club == 'Real Madrid']['Overall'] revs = df.loc[df.Club == 'New England Revolution']['Overall']plt.figure(figsize=(6, 8))plt.style.use('default') # plt.style.use('ggplot')labels = ['FC Barcelona', 'Real Madrid', 'New England Revolution'] plt.title('Professional Soccer Team Comparison') plt.ylabel('FIFA Overall Rating')boxes = plt.boxplot([barcelona, madrid, revs],labels=labels,patch_artist=True )for box in boxes['boxes']: # 設(shè)置方框的樣式box.set(color='#ff0000', linewidth=2) # 設(shè)置填充的樣式, 要設(shè)置 patch_artist=Truebox.set(facecolor='#00ff00')plt.show()

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