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

--- python:first_course_statistics [2016/10/08 09:11]
Francesco Beretta [Eruptions of the Old Faithful geyser (p.5)]
+++ python:first_course_statistics [2016/10/28 14:36]
Beretta, Anna Letizia
@@ Ligne 1: / Ligne 1: @@
-====== Lessons ======
+====== General instructions ======
+Read following important documentation about:
+  * pandas [[http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe|dataframes]]
+  * [[http://matplotlib.org/api/pyplot_summary.html|matplotlib.pyplot]]
-===== Eruptions of the "Old Faithful" geyser (p.5) =====
+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'
+\\
+====== Eruptions of the "Old Faithful" geyser (p.5) ======
+\\
-==== Histogram (p.5) ====
+===== Histogram (p.5) =====
 <code python>
-# fake code – to be deleted
+import pandas as pd
-import csv
+import matplotlib.pyplot as plt
-filename = 'ch02-data.csv'
+gys1 = pd.DataFrame(pd.read_csv('../geyser1.TAB', '\t'))
-f = open(filename)
+g_int = gys1['Interval']
-data = []
+ax = plt.gca()
-reader = csv.reader(f)
+ax.hist(g_int, bins=20, color='r')
-header = reader.next()
+ax.set_xlabel('Intereruption time')
-data = [row for row in reader]
+ax.set_ylabel('Frequency')
-for datarow in data:
+ax.set_title('Histogram')
-    print datarow
+plt.show()
 </code>
-===== International adoption rates (p.13) =====
+\\
+===== Boxplot (p. 6) =====
+<code python>
+import matplotlib.pyplot as plt
+import pandas as pd
+gysr1_boxplot = pd.read_csv('...\geyser1.TAB', '\t')
+data_gysr1 = gysr1_boxplot['Interval']
+plt.boxplot(data_gysr1)
+ax = plt.gca()
+ax.set_xlabel('222 cases')
+ax.set_ylabel('Interruption time ( minutes')
+ax.set_title('Box and Whisker Plot')
+plt.show()
+</code>
+\\
+===== ScatterPlot (p. 7) =====
+AB: Put face- and edgecolor to change both of them. You can also have two different colors for the in- and outside of each dot.
+<code python>
+import matplotlib.pyplot as plt
+import pandas as pd
+geysr1_scatterplot = pd.read_csv('...\geyser1.TAB', '\t')
+geysr1_data_Xax = geysr1_scatterplot['Duration']
+geysr1_data_Yax = geysr1_scatterplot['Interval']
+plt.scatter(geysr1_data_Xax, geysr1_data_Yax, facecolor='y', edgecolor='y')
+ax = plt.gca()
+ax.set_xlabel('Eruption duration time (minutes)')
+ax.set_ylabel('Interuption time (minutes)')
+ax.set_title('Scatter Plot of INTERVAL vs DURATION')
+plt.show()
+</code>
+\\
+===== Descriptive statistics (p.9) =====
+Note: try different examples, e.g. the whole population or only those where 'Duration' <= 3, the whole dataframe
+[[http://pandas.pydata.org/pandas-docs/stable/basics.html#descriptive-statistics|doc]] – [[http://www.marsja.se/pandas-python-descriptive-statistics/|example]]
+<code python>
+import pandas as pd
+gysr1 = pd.read_csv('../geyser1.tab', '\t')
+gysr1['Duration'][gysr1['Duration'] <= 3].describe()
+</code>
+\\
+===== Boxplot (p.9) =====
+Selecting rows in a dataframe: [[http://pandas.pydata.org/pandas-docs/stable/indexing.html#the-where-method-and-masking|doc]] / [[http://stackoverflow.com/questions/17071871/select-rows-from-a-dataframe-based-on-values-in-a-column-in-pandas|example]]
+<code python>
+import matplotlib.pyplot as plt
+import pandas as pd
+gysr1 = pd.read_csv('../geyser1.tab', '\t')
+gysr1_inf3 = gysr1.loc[gysr1['Duration'] <= 3]
+gysr1_sup3 = gysr1.loc[gysr1['Duration'] > 3]
+plt.boxplot([gysr1_inf3['Interval'],gysr1_sup3['Interval']], labels= ['inf3','sup3'])
+</code>
+\\
+====== International adoption rates (p.13) ======
+===== Boxplot (p.14) =====
+<code python>
+import matplotlib.pyplot as plt
+import pandas as pd
+adopt_data = pd.read_csv('D:\Python\Libri\A_Casebook_for_a_First_Course_in_Statistics_and_Data_Analysis_Datasets\Data\Tab\\adopt.TAB', '\t')
+adopt1 = adopt_data['Visa91']
+plt.boxplot(adopt1)
+ax = plt.gca()
+ax.set_title('Box and Whisker Plot')
+ax.set_xlabel('39 cases')
+ax.set_ylabel('Number of visas in 1991')
+plt.show()
+</code>
+\\
+===== Histogram (p.14) =====
+<code python>
+import matplotlib.pyplot as plt
+import pandas as pd
+adopt_data = pd.read_csv('D:\Python\Libri\A_Casebook_for_a_First_Course_in_Statistics_and_Data_Analysis_Datasets\Data\Tab\\adopt.TAB', '\t')
+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) =====

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