Outils pour utilisateurs
python:first_course_statistics
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python:first_course_statistics [2016/10/08 09:11] Francesco Beretta [Eruptions of the Old Faithful geyser (p.5)] |
python:first_course_statistics [2016/10/17 07:58] Francesco Beretta [Descriptive statistics] |
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| 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> | <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> | </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) ====== | ||
python/first_course_statistics.txt · Dernière modification: 2017/09/26 08:54 par Francesco Beretta
