Différences

Ci-dessous, les différences entre deux révisions de la page.

--- python:first_course_statistics [2016/10/19 14:57] – alberetta
+++ python:first_course_statistics [2017/09/26 08:54] (Version actuelle) – [General instructions] fberetta
@@ Ligne 5: / Ligne 5: @@
   * pandas [[http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe|dataframes]]
   * [[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'
@@ Ligne 35: / Ligne 39: @@
 import matplotlib.pyplot as plt
 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']
 plt.boxplot(data_gysr1)
@@ Ligne 55: / Ligne 59: @@
 import matplotlib.pyplot as plt
 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_Yax = geysr1_scatterplot['Interval']
@@ Ligne 132: / Ligne 136: @@
 adopt1 = adopt_data['Visa91']
 plt.hist(adopt1)
+plt.show()
+</code>
+\\
+=====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>
+=====Scatterplot (p. 17)=====
+<code python>
+import matplotlib.pyplot as plt
+import pandas as pd
+adoption_scatterplot = pd.read_csv('...\adopt.TAB', '\t')
+adopt_data_Xax = adoption_scatterplot['Visa88']
+adopt_data_Yax = adoption_scatterplot['Visa91']
+plt.scatter(adopt_data_Xax, adopt_data_Yax, facecolor='y', edgecolor='y')
+ax = plt.gca()
+ax.set_xlabel('Number of Visas in 1988')
+ax.set_ylim([0,2700])
+ax.set_xlim([0,5000])
+ax.set_ylabel('Number of Visas in 1991')
+ax.set_title('ScatterPlot of Visa91 vs Visa88')
+plt.show()
+</code>
+\\
+=====Scatterplot (p.18)=====
+<code python>
+import matplotlib.pyplot as plt
+import pandas as pd
+adoption_scatterplot = pd.read_csv('D:\Python\Libri\A_Casebook_for_a_First_Course_in_Statistics_and_Data_Analysis_Datasets\Data\Tab\\adopt.TAB', '\t')
+adopt_data_Xax = adoption_scatterplot['Visa91']
+adopt_data_Yax = adoption_scatterplot['Visa92']
+plt.scatter(adopt_data_Xax, adopt_data_Yax, facecolor='y', edgecolor='y')
+ax = plt.gca()
+ax.set_xlabel('Number of Visas in 1991')
+ax.set_ylim([0,1800])
+ax.set_xlim([0,2700])
+ax.set_ylabel('Number of Visas in 1992')
+ax.set_title('ScatterPlot of Visa92 vs Visa91')
+plt.show()
+</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>