Update 3_3_Data_Munging_with_Pandas.ipynb

Updated to last version of scikit

ml1/2_0_0_Intro_ML.ipynb

@@ -71,7 +71,6 @@
    "source": [
     "* [Scikit-learn web page](http://scikit-learn.org/stable/)\n",
     "* [Scikit-learn videos](http://blog.kaggle.com/author/kevin-markham/) and [notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n",
-    "* [scikit-learn : Machine Learning Simplified](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), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2017.\n",
     "* [Python Machine Learning](https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/), Sebastian Raschka, Packt Publishing, 2019."
    ]
   },

ml1/2_0_1_Objectives.ipynb

@@ -63,9 +63,7 @@
    "metadata": {},
    "source": [
     "* [Scikit-learn web page](http://scikit-learn.org/stable/)\n",
-    "* [Scikit-learn videos](http://blog.kaggle.com/author/kevin-markham/) and [notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n",
-    "* [scikit-learn : Machine Learning Simplified](https://learning.oreilly.com/library/view/scikit-learn-machine/9781788833479/), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2017.\n",
-    "* [Python Machine Learning](https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/), Sebastian Raschka, Packt Publishing, 2019."
+    "* [Scikit-learn videos](http://blog.kaggle.com/author/kevin-markham/) and [notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n"
    ]
   },
   {

ml1/2_3_0_Visualisation.ipynb

@@ -228,7 +228,6 @@
    "source": [
     "* [Feature selection](http://scikit-learn.org/stable/modules/feature_selection.html)\n",
     "* [Classification probability](http://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html)\n",
-    "* [Mastering Pandas](https://learning.oreilly.com/library/view/mastering-pandas/9781789343236/), Femi Anthony, Packt Publishing, 2015.\n",
     "* [Matplotlib web page](http://matplotlib.org/index.html)\n",
     "* [Using matlibplot in IPython](http://ipython.readthedocs.org/en/stable/interactive/plotting.html)\n",
     "* [Seaborn Tutorial](https://stanford.edu/~mwaskom/software/seaborn/tutorial.html)\n",

ml1/2_3_1_Advanced_Visualisation.ipynb

@@ -408,7 +408,6 @@
    "source": [
     "* [Feature selection](http://scikit-learn.org/stable/modules/feature_selection.html)\n",
     "* [Classification probability](http://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html)\n",
-    "* [Mastering Pandas](https://learning.oreilly.com/library/view/mastering-pandas/9781789343236/), Femi Anthony, Packt Publishing, 2015.\n",
     "* [Matplotlib web page](http://matplotlib.org/index.html)\n",
     "* [Using matlibplot in IPython](http://ipython.readthedocs.org/en/stable/interactive/plotting.html)\n",
     "* [Seaborn Tutorial](https://stanford.edu/~mwaskom/software/seaborn/tutorial.html)\n",

ml1/2_4_Preprocessing.ipynb

@@ -163,7 +163,6 @@
    "source": [
     "* [Feature selection](http://scikit-learn.org/stable/modules/feature_selection.html)\n",
     "* [Classification probability](http://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html)\n",
-    "* [Mastering Pandas](https://learning.oreilly.com/library/view/mastering-pandas/9781789343236/), Femi Anthony, Packt Publishing, 2015.\n",
     "* [Matplotlib web page](http://matplotlib.org/index.html)\n",
     "* [Using matlibplot in IPython](http://ipython.readthedocs.org/en/stable/interactive/plotting.html)\n",
     "* [Seaborn Tutorial](https://stanford.edu/~mwaskom/software/seaborn/tutorial.html)"

ml1/2_5_0_Machine_Learning.ipynb

@@ -154,7 +154,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* [General concepts of machine learning with scikit-learn](https://ogrisel.github.io/scikit-learn.org/sklearn-tutorial/auto_examples/tutorial/plot_ML_flow_chart.html)\n",
+    "* [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/index.html)\n",
     "* [A Tour of Machine Learning Algorithms](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/)"
    ]
   },

ml1/2_5_1_kNN_Model.ipynb

@@ -379,8 +379,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* [KNeighborsClassifier API scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)\n",
-    "* [Learning scikit-learn: Machine Learning in Python](https://learning.oreilly.com/library/view/scikit-learn-machine/9781788833479/), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n"
+    "* [KNeighborsClassifier API scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)\n"
    ]
   },
   {

ml1/2_5_2_Decision_Tree_Model.ipynb

@@ -509,8 +509,6 @@
    "metadata": {},
    "source": [
     "* [Plot the decision surface of a decision tree on the iris dataset](https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html)\n",
-    "* [scikit-learn : Machine Learning Simplified](https://learning.oreilly.com/library/view/scikit-learn-machine/9781788833479/), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2017.\n",
-    "* [Python Machine Learning](https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/), Sebastian Raschka, Packt Publishing, 2019.\n",
     "* [Parameter estimation using grid search with cross-validation](https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_digits.html)\n",
     "* [Decision trees in python with scikit-learn and pandas](http://chrisstrelioff.ws/sandbox/2015/06/08/decision_trees_in_python_with_scikit_learn_and_pandas.html)"
    ]

ml1/2_6_Model_Tuning.ipynb

@@ -518,8 +518,6 @@
    "metadata": {},
    "source": [
     "* [Plot the decision surface of a decision tree on the iris dataset](https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html)\n",
-    "* [scikit-learn : Machine Learning Simplified](https://learning.oreilly.com/library/view/scikit-learn-machine/9781788833479/), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2017.\n",
-    "* [Python Machine Learning](https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/), Sebastian Raschka, Packt Publishing, 2019.\n",
     "* [Hyperparameter estimation using grid search with cross-validation](http://scikit-learn.org/stable/auto_examples/model_selection/grid_search_digits.html)\n",
     "* [Decision trees in python with scikit-learn and pandas](http://chrisstrelioff.ws/sandbox/2015/06/08/decision_trees_in_python_with_scikit_learn_and_pandas.html)"
    ]

ml1/util_ds.py

@@ -47,7 +47,7 @@ def get_code(tree, feature_names, target_names,
 
     recurse(left, right, threshold, features, 0, 0)
 
-# Taken from http://scikit-learn.org/stable/auto_examples/tree/plot_iris.html#example-tree-plot-iris-py
+# Taken from https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -114,4 +114,4 @@ def plot_tree_iris():
 
     plt.suptitle("Decision surface of a decision tree using paired features")
     plt.legend()
-    plt.show()  
+    plt.show()  

ml2/3_0_0_Intro_ML_2.ipynb

@@ -74,9 +74,7 @@
    "metadata": {},
    "source": [
     "* [IPython Notebook Tutorial for Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic/forums/t/5105/ipython-notebook-tutorial-for-titanic-machine-learning-from-disaster)\n",
-    "* [Scikit-learn videos and notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n",
-    "* [Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits](https://learning.oreilly.com/library/view/hands-on-machine-learning/9781838826048/), Tarek Amr, Packt Publishing, 2020.\n",
-    "* [Python Machine Learning](https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/), Sebastian Raschka and Vahid Mirjalili, Packt Publishing, 2019."
+    "* [Scikit-learn videos and notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n"
    ]
   },
   {

ml2/3_1_Read_Data.ipynb

@@ -213,8 +213,7 @@
     "* [Pandas API input-output](http://pandas.pydata.org/pandas-docs/stable/api.html#input-output)\n",
     "* [Pandas API - pandas.read_csv](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html)\n",
     "* [DataFrame](http://pandas.pydata.org/pandas-docs/stable/dsintro.html)\n",
-    "* [An introduction to NumPy and Scipy](https://sites.engineering.ucsb.edu/~shell/che210d/numpy.pdf)\n",
-    "* [NumPy tutorial](https://numpy.org/doc/stable/)"
+    "* [An introduction to NumPy and Scipy](https://sites.engineering.ucsb.edu/~shell/che210d/numpy.pdf)\n"
    ]
   },
   {

ml2/3_2_Pandas.ipynb

@@ -433,7 +433,6 @@
    "metadata": {},
    "source": [
     "* [Pandas](http://pandas.pydata.org/)\n",
-    "* [Learning Pandas, Michael Heydt, Packt Publishing, 2017](https://learning.oreilly.com/library/view/learning-pandas/9781787123137/)\n",
     "* [Pandas. Introduction to Data Structures](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html)\n",
     "* [Introducing Pandas Objects](https://www.oreilly.com/learning/introducing-pandas-objects)\n",
     "* [Boolean Operators in Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-operators)"

ml2/3_3_Data_Munging_with_Pandas.ipynb

@@ -373,8 +373,8 @@
    "source": [
     "#Mean age of  passengers per Passenger class\n",
     "\n",
-    "#First we calculate the mean\n",
-    "df.groupby('Pclass').mean()"
+    "#First we calculate the mean for the numeric columns\n",
+    "df.select_dtypes(np.number).groupby('Pclass').mean()"
    ]
   },
   {