added to data organizing

doc/src/How2ReadData/How2ReadData.do.txt

@@ -6,6 +6,48 @@ DATE: today
 !split
 ===== Representing data, overarching aims  =====
 !bblock
-
+!bc pycod 
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import sparse
+import pandas as pd
+from IPython.display import display
+eye = np.eye(4)
+print(eye)
+sparse_mtx = sparse.csr_matrix(eye)
+print(sparse_mtx)
+x = np.linspace(-10,10,100)
+y = np.sin(x)
+plt.plot(x,y,marker='x')
+plt.show()
+data = {'Name': ["John", "Anna", "Peter", "Linda"], 'Location': ["Roma", "Napoli", "Torino", "Milano"], 'Age':[51, 21, 34, 45]}
+data_pandas = pd.DataFrame(data)
+display(data_pandas)
+!ec
+!eblock
+
+
+
+!split
+===== Representing data, overarching aims  =====
+!bblock
+!bc pycod 
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import sparse
+import pandas as pd
+from IPython.display import display
+import mglearn
+import sklearn
+from sklearn.linear_model import LinearRegression
+from sklearn.tree import DecisionTreeRegressor
+x, y = mglearn.datasets.make_wave(n_samples=100)
+line = np.linspace(-3,3,1000,endpoint=False).reshape(-1,1)
+reg = DecisionTreeRegressor(min_samples_split=3).fit(x,y)
+plt.plot(line, reg.predict(line), label="decision tree")
+regline = LinearRegression().fit(x,y)
+plt.plot(line, regline.predict(line), label= "Linear Rgression")
+plt.show()
+!ec
 !eblock