Merge branch 'master' of https://git.wmi.amu.edu.pl/tdwojak/Python2017

# Conflicts:
    #	labs02/test_task.py

labs06/Pandas.ipynb

@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "collapsed": true,
     "slideshow": {
@@ -299,30 +299,12 @@
    ]
   },
   {
-   "cell_type": "code",
+   "cell_type": "markdown",
-   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    26.000000\n",
-      "mean      8.576923\n",
-      "std       5.375300\n",
-      "min       1.000000\n",
-      "25%       4.250000\n",
-      "50%       8.000000\n",
-      "75%      12.000000\n",
-      "max      18.000000\n",
-      "dtype: float64\n"
-     ]
-    }
-   ],
    "source": [
     "print(dane.describe())"
    ]

labs06/task02.py

@@ -1,14 +1,15 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
+import pandas as pd
 
 def wczytaj_dane():
-    pass
+    return pd.read_csv('mieszkania.csv')
 
 def most_common_room_number(dane):
-    pass
+    return dane['Rooms'].mode()[0]
 
 def cheapest_flats(dane, n):
-    pass
+    return dane.sort_values(by=[u'Expected'], na_position='last').head(n)
 
 def find_borough(desc):
     dzielnice = ['Stare Miasto',
@@ -19,23 +20,54 @@ def find_borough(desc):
                  'Winogrady',
                  'Miłostowo',
                  'Dębiec']
-    pass
+    histogram = {districtName: desc.find(districtName) for districtName in dzielnice if desc.find(districtName) != -1}
+    return 'Inne' if not histogram else min(histogram, key=histogram.get)
 
 
 def add_borough(dane):
-    pass
+    dane['Borough'] = dane.apply( lambda row: find_borough( row['Location']), axis = 1)
 
 def write_plot(dane, filename):
-    pass
+    dane['Borough'].value_counts().plot.bar().get_figure().savefig(filename)
 
 def mean_price(dane, room_number):
-    pass
+    return dane.loc[ dane['Rooms'] == room_number ].mean()[1]
 
 def find_13(dane):
-    pass
+    return dane.loc[dane['Floor'] == 13]['Borough'].unique()
 
 def find_best_flats(dane):
-    pass
+    return dane.loc[(dane['Borough'] == 'Winogrady') & (dane['Rooms'] == 3) & (dane['Floor'] == 1)]
+
+def predict(dane, col_name):
+    from sklearn import linear_model
+    from sklearn.metrics import mean_squared_error, r2_score
+    d_X = pd.DataFrame(dane[col_name])
+    print('Dane z kolumny ', col_name)
+    print(d_X.head())
+    d_X_train = d_X[4000:]
+    d_X_test = d_X[:4000]
+    d_y = pd.DataFrame(dane['Expected'])
+    d_y_train = d_y[4000:]
+    d_y_test = d_y[:4000]
+    regr = linear_model.LinearRegression()
+    regr.fit(d_X_train, d_y_train)
+    y_pred = regr.predict(d_X_test)
+    print('MODEL(%s): pred_y = %f * x + %f' % (col_name, regr.coef_[0], regr.intercept_) )
+    print('Mean squared error: %.2f' % mean_squared_error(d_y_test, y_pred))
+
+    import matplotlib.pyplot as plt
+
+    plt.clf()
+    dataLine, =  plt.plot(d_X_test, d_y_test, 'ro', label='collected data')
+    predLine, = plt.plot(d_X_test, y_pred, color='blue', linestyle='--', linewidth = 2, label='predictions')
+    ax = plt.gca().add_artist(plt.legend(handles=[dataLine], loc=1))
+    plt.legend(handles=[predLine], loc=4)
+    plt.xticks(())
+    plt.yticks(())
+    plt.xlabel(col_name)
+    plt.ylabel('Price')
+    plt.show()
 
 def main():
     dane = wczytaj_dane()
@@ -44,11 +76,44 @@ def main():
     print("Najpopularniejsza liczba pokoi w mieszkaniu to: {}"
           .format(most_common_room_number(dane)))
 
-    print("{} to najłądniejsza dzielnica w Poznaniu."
+    print("7 najtańszych mieszkań to: ")
-          .format(find_borough("Grunwald i Jeżyce"))))
+    print(cheapest_flats(dane, 7))
 
-    print("Średnia cena mieszkania 3-pokojowego, to: {}"
+    print("{} to najłądniejsza dzielnica w Poznaniu.".format(find_borough("Grunwald i Jeżyce")))
-          .format(mean_price(dane, 3)))
+
+    add_borough(dane)
+    write_plot(dane, 'tmp_borough_hist.png')
+    for i in dane['Rooms'].unique():
+        print("Średnia cena mieszkania {}-pokojowego, to: {}".format(i, mean_price(dane, i)))
+    print('Dzielnice z mieszkaniami na 13 piętrze: {}'.format(find_13(dane)))
+    print('"Najlepsze" mieszkania: ')
+    print(find_best_flats(dane))
+
+    predict(dane, 'Rooms')
+    predict(dane, 'SqrMeters')
+
+
+    from sklearn import datasets, linear_model
+    import numbers as np
+    from sklearn.metrics import mean_squared_error, r2_score
+
+
+
+
+    #
+    # diabetes = datasets.load_diabetes()
+    # diabetes_X = diabetes.data[:, None, 3]
+    # diabetes_X_train = diabetes_X[:-20]
+    # diabetes_X_test = diabetes_X[-20:]
+    # diabetes_y_train = diabetes.target[:-20]
+    # diabetes_y_test = diabetes.target[-20:]
+    # regr = linear_model.LinearRegression()
+    # regr.fit(diabetes_X_train, diabetes_y_train)
+    # diabetes_y_pred = regr.predict(diabetes_X_test)
+    # print('Coefficients: \n', regr.coef_)
+    # print('Mean squared error: %.2f' % mean_squared_error(diabetes_y_test, diabetes_y_pred))
+    #
+    # print( diabetes.data[:, None, 3].shape )
 
 if __name__ == "__main__":
     main()