Outils pour utilisateurs
python:first_course_statistics
Différences
Ci-dessous, les différences entre deux révisions de la page.
| Les deux révisions précédentes Révision précédente Prochaine révision | Révision précédente Dernière révision Les deux révisions suivantes | ||
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python:first_course_statistics [2016/10/28 14:36] Beretta, Anna Letizia |
python:first_course_statistics [2017/02/09 14:27] Francesco Beretta [Boxplot (p. 6)] |
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|---|---|---|---|
| Ligne 35: | Ligne 35: | ||
| import matplotlib.pyplot as plt | import matplotlib.pyplot as plt | ||
| import pandas as pd | import pandas as pd | ||
| - | gysr1_boxplot = pd.read_csv('...\geyser1.TAB', '\t') | + | gysr1_boxplot = pd.read_csv('.../geyser1.TAB', '\t') |
| data_gysr1 = gysr1_boxplot['Interval'] | data_gysr1 = gysr1_boxplot['Interval'] | ||
| plt.boxplot(data_gysr1) | plt.boxplot(data_gysr1) | ||
| Ligne 55: | Ligne 55: | ||
| import matplotlib.pyplot as plt | import matplotlib.pyplot as plt | ||
| import pandas as pd | import pandas as pd | ||
| - | geysr1_scatterplot = pd.read_csv('...\geyser1.TAB', '\t') | + | geysr1_scatterplot = pd.read_csv('.../geyser1.TAB', '\t') |
| geysr1_data_Xax = geysr1_scatterplot['Duration'] | geysr1_data_Xax = geysr1_scatterplot['Duration'] | ||
| geysr1_data_Yax = geysr1_scatterplot['Interval'] | geysr1_data_Yax = geysr1_scatterplot['Interval'] | ||
| Ligne 242: | Ligne 242: | ||
| =====Scatter Plot of PRODJAPN vs QUALJAPN (p. 27) ===== | =====Scatter Plot of PRODJAPN vs QUALJAPN (p. 27) ===== | ||
| + | <code Python> | ||
| + | import pandas as pd | ||
| + | import matplotlib.pyplot as plt | ||
| + | scatter_plot = pd.read_csv('D:\Python\Libri\A_Casebook_for_a_First_Course_in_Statistics_and_Data_Analysis_Datasets\Data\Tab\\prdq.TAB', '\t') | ||
| + | productivity_Y = scatter_plot['ProdJapn'] | ||
| + | quality_X = scatter_plot['QualJapn'] | ||
| + | plt.scatter(productivity_Y, quality_X, bins=20, colors='r') | ||
| + | ax = plt.gca() | ||
| + | ax.set_Xlabel('Assembly defects per 100 cars (Japanese origin)') | ||
| + | ax.set_Ylabel('Hours per vehicle (Japanese origin') | ||
| + | ax.set_title('Scatter Plot of PRODJAPN VS QUALJAPN') | ||
| + | plt.show() | ||
| + | </code> | ||
| + | |||
| + | |||
| + | =====Scatter Plot of PRODNONJ cs QUALNONJ (p. 27)===== | ||
| + | <code Python> | ||
| + | import pandas as pd | ||
| + | import matplotlib.pyplot as plt | ||
| + | scatter_plot = pd.read_csv('D:\Python\Libri\A_Casebook_for_a_First_Course_in_Statistics_and_Data_Analysis_Datasets\Data\Tab\\prdq.TAB', '\t') | ||
| + | productivity_Y = scatter_plot['ProdNonJ'] | ||
| + | quality_X = scatter_plot['QualNonJ'] | ||
| + | plt.scatter(productivity_Y, quality_X, bins=20, colors='r') | ||
| + | ax = plt.gca() | ||
| + | ax.set_Xlabel('Assembly defects per 100 cars (non-Japanese origin)') | ||
| + | ax.set_Ylabel('Hours per vehicle (non-Japanese origin') | ||
| + | ax.set_title('Scatter Plot of PRODNONJ VS QUALNONJ') | ||
| + | plt.show() | ||
| + | </code> | ||
| + | |||
| + | |||
| + | |||
| + | ===== Scatterplot of productivity VS quality (p. 28) ===== | ||
| + | <code python> | ||
| + | import pandas as pd | ||
| + | import matplotlib.pyplot as plt | ||
| + | scatter_plot = pd.read_csv('D:\Python\Libri\A_Casebook_for_a_First_Course_in_Statistics_and_Data_Analysis_Datasets\Data\Tab\\prdq.TAB', '\t') | ||
| + | productivity_Y = scatter_plot['Producti'] | ||
| + | quality_X = scatter_plot['Quality'] | ||
| + | plt.scatter(productivity_Y, quality_X, bins=20, colors='r') | ||
| + | ax = plt.gca() | ||
| + | ax.set_Xlabel('Assembly defects per 100 cars') | ||
| + | ax.set_Ylabel('Hours per vehicle') | ||
| + | ax.set_title('Scatter Plot of PRODUCTIVITY VS QUALITY') | ||
| + | plt.show() | ||
| + | </code> | ||
| + | |||
| + | |||
| + | ===== Productivity versus quality in the assembly plant (p.29) ===== | ||
| + | |||
| + | It worked the first time but now it doesn't work again. Maybe again a windows error? | ||
| + | |||
| + | <code python> | ||
| + | #1 | ||
| + | import matplotlib.pyplot as plt | ||
| + | import pandas as pd | ||
| + | data_comparison = pd.read_csv('D:\Python\Libri\A_Casebook_for_a_First_Course_in_Statistics_and_Data_Analysis_Datasets\Data\Tab\prdq.TAB', '\t') | ||
| + | non_japanese = data_comparison.loc[data_comparison['QualNonJ']] | ||
| + | japanese = data_comparison.loc[data_comparison['QualJapn']] | ||
| + | plt.boxplot([non_japanese['Quality'],japanese['Quality']], labels= ['Non-japanese','Japanese']) | ||
| + | plt.show() | ||
| + | |||
| + | #2 | ||
| + | import matplotlib.pyplot as plt | ||
| + | import pandas as pd | ||
| + | data_comparison = pd.read_csv('D:\Python\Libri\A_Casebook_for_a_First_Course_in_Statistics_and_Data_Analysis_Datasets\Data\Tab\prdq.TAB', '\t') | ||
| + | non_japanese = data_comparison.loc[data_comparison['ProdNonJ']] | ||
| + | japanese = data_comparison.loc[data_comparison['ProdJapn']] | ||
| + | plt.boxplot([non_japanese['Producti'],japanese['Producti']], labels= ['Non-japanese','Japanese']) | ||
| + | plt.show() | ||
| + | </code> | ||
python/first_course_statistics.txt · Dernière modification: 2017/09/26 08:54 par Francesco Beretta
