Ci-dessous, les différences entre deux révisions de la page.
Les deux révisions précédentes Révision précédente Prochaine révision | Révision précédente | ||
python:first_course_statistics [2016/10/24 14:30] Beretta, Anna Letizia [Scatterplot (p.18)] |
python:first_course_statistics [2017/09/26 08:54] (Version actuelle) Francesco Beretta [General instructions] |
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* pandas [[http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe|dataframes]] | * pandas [[http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe|dataframes]] | ||
* [[http://matplotlib.org/api/pyplot_summary.html|matplotlib.pyplot]] | * [[http://matplotlib.org/api/pyplot_summary.html|matplotlib.pyplot]] | ||
+ | |||
+ | |||
+ | Get the data from [[http://people.stern.nyu.edu/jsimonof/Casebook/Data/ASCII/README.html|this site]]. | ||
+ | |||
Save your scripts in a folder inside the data folder, calling the script folder 'my_scripts' or whaterver. If 'my-scripts' is set as your [[python:generic_features#get_the_current_working_directory_address|current working directory]], then the data files are available under this address '../[data file]', for instantce: '../geyser1.TAB' | Save your scripts in a folder inside the data folder, calling the script folder 'my_scripts' or whaterver. If 'my-scripts' is set as your [[python:generic_features#get_the_current_working_directory_address|current working directory]], then the data files are available under this address '../[data file]', for instantce: '../geyser1.TAB' | ||
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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) | ||
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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'] | ||
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\\ | \\ | ||
+ | |||
+ | =====Histogram with Log(p.18)===== | ||
+ | don't find the way to do it | ||
+ | <code Python> | ||
+ | import pandas as pd | ||
+ | import matplotlib.pyplot as plt | ||
+ | adopt = pd.DataFrame(pd.read_csv('D:\Python\Libri\A_Casebook_for_a_First_Course_in_Statistics_and_Data_Analysis_Datasets\Data\Tab\\adopt.TAB', '\t')) | ||
+ | adopt_loghist = adopt['Visa91'] | ||
+ | #adopt_loghist.semilogx() --> was one of the possibilities | ||
+ | ax = plt.gca() | ||
+ | ax.hist(adopt_loghist, bins=10, plt.loglog(0.5,3.5), color='r') #put log=True instead, but you will get the log for the frequencies | ||
+ | plt.gca().set_xscale("log") | ||
+ | ax.set_xlabel('Log (Number of 1991 visas') | ||
+ | ax.set_ylabel('Frequency') | ||
+ | ax.set_title('Histogram') | ||
+ | plt.show() | ||
+ | </code> | ||
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</code> | </code> | ||
+ | \\ | ||
+ | |||
+ | ====== The Performance of stock mutual funds (p. 21) ====== | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | \\ | ||
+ | |||
+ | ====== Predicting the sales and airplay of popular music (p. 23)====== | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | \\ | ||
+ | |||
+ | ====== Another look at the "Old faithful" geyser and adoption visas (p.24) ====== | ||
+ | |||
+ | Modified the bins of the both histograms: | ||
+ | The Histogram is reliable for the "Old faithful" geyser but not for the Adoption rates. The appearance of the histogram changes quite a lot by changing the bins. | ||
+ | |||
+ | \\ | ||
+ | |||
+ | ====== Productivity versus quality in the assembly plant (p. 25)====== | ||
+ | |||
+ | |||
+ | ===== Scatterplot of Productivity vs Quality (p. 26) ===== | ||
+ | <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> | ||
+ | |||
+ | \\ | ||
+ | |||
+ | =====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> |