Update notebooks to latest nbformat

2026-01-14 12:14:36 +01:00 · 2020-04-06 19:13:12 +12:00
parent d507ec815a
commit 2f7ab70295
22 changed files with 112 additions and 194 deletions
--- a/01_the_machine_learning_landscape.ipynb
+++ b/01_the_machine_learning_landscape.ipynb
@@ -785,7 +785,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
  },
  "nav_menu": {},
  "toc": {
@@ -806,5 +806,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/02_end_to_end_machine_learning_project.ipynb
+++ b/02_end_to_end_machine_learning_project.ipynb
@@ -1664,9 +1664,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "Question: Try a Support Vector Machine regressor (`sklearn.svm.SVR`), with various hyperparameters such as `kernel=\"linear\"` (with various values for the `C` hyperparameter) or `kernel=\"rbf\"` (with various values for the `C` and `gamma` hyperparameters). Don't worry about what these hyperparameters mean for now. How does the best `SVR` predictor perform?"
   ]
@@ -2170,7 +2168,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
  },
  "nav_menu": {
   "height": "279px",
@@ -2188,5 +2186,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/03_classification.ipynb
+++ b/03_classification.ipynb
@@ -1163,9 +1163,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "# Exercise solutions"
   ]
@@ -2553,7 +2551,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
  },
  "nav_menu": {},
  "toc": {
@@ -2567,5 +2565,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/04_training_linear_models.ipynb
+++ b/04_training_linear_models.ipynb
@@ -1797,7 +1797,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
  },
  "nav_menu": {},
  "toc": {
@@ -1811,5 +1811,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/05_support_vector_machines.ipynb
+++ b/05_support_vector_machines.ipynb
@@ -403,9 +403,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "# Non-linear classification"
   ]
@@ -1241,9 +1239,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "See appendix A."
   ]
@@ -1834,7 +1830,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
  },
  "nav_menu": {},
  "toc": {
@@ -1848,5 +1844,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/06_decision_trees.ipynb
+++ b/06_decision_trees.ipynb
@@ -465,9 +465,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "# Exercise solutions"
   ]
@@ -488,9 +486,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "## 7."
   ]
@@ -733,7 +729,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
  },
  "nav_menu": {
   "height": "309px",
@@ -750,5 +746,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/07_ensemble_learning_and_random_forests.ipynb
+++ b/07_ensemble_learning_and_random_forests.ipynb
@@ -924,9 +924,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "# Exercise solutions"
   ]
@@ -1394,7 +1392,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
  },
  "nav_menu": {
   "height": "252px",
@@ -1411,5 +1409,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/08_dimensionality_reduction.ipynb
+++ b/08_dimensionality_reduction.ipynb
@@ -212,9 +212,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "Notice that running PCA multiple times on slightly different datasets may result in different results. In general the only difference is that some axes may be flipped. In this example, PCA using Scikit-Learn gives the same projection as the one given by the SVD approach, except both axes are flipped:"
   ]
@@ -1481,9 +1479,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "# Exercise solutions"
   ]
@@ -1504,9 +1500,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "## 9."
   ]
@@ -1917,9 +1911,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "*Exercise: Alternatively, you can write colored digits at the location of each instance, or even plot scaled-down versions of the digit images themselves (if you plot all digits, the visualization will be too cluttered, so you should either draw a random sample or plot an instance only if no other instance has already been plotted at a close distance). You should get a nice visualization with well-separated clusters of digits.*"
   ]
@@ -2264,9 +2256,9 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
  }
 },
 "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
--- a/09_unsupervised_learning.ipynb
+++ b/09_unsupervised_learning.ipynb
@@ -975,9 +975,7 @@
  {
   "cell_type": "code",
   "execution_count": 47,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "minibatch_kmeans = MiniBatchKMeans(n_clusters=10, batch_size=10, random_state=42)\n",
@@ -1418,9 +1416,7 @@
  {
   "cell_type": "code",
   "execution_count": 71,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 3.2))\n",
@@ -1706,9 +1702,7 @@
  {
   "cell_type": "code",
   "execution_count": 91,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "grid_clf.score(X_test, y_test)"
@@ -3792,9 +3786,9 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
  }
 },
 "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
--- a/10_neural_nets_with_keras.ipynb
+++ b/10_neural_nets_with_keras.ipynb
@@ -1597,9 +1597,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "# Exercise solutions"
   ]
@@ -1613,9 +1611,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "See appendix A."
   ]
@@ -2015,7 +2011,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
  },
  "nav_menu": {
   "height": "264px",
@@ -2032,5 +2028,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/11_training_deep_neural_networks.ipynb
+++ b/11_training_deep_neural_networks.ipynb
@@ -2103,9 +2103,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "# Exercises"
   ]
@@ -2684,7 +2682,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
  },
  "nav_menu": {
   "height": "360px",
@@ -2701,5 +2699,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/12_custom_models_and_training_with_tensorflow.ipynb
+++ b/12_custom_models_and_training_with_tensorflow.ipynb
@@ -1012,9 +1012,7 @@
  {
   "cell_type": "code",
   "execution_count": 78,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X_train_scaled, y_train, epochs=2,\n",
@@ -1158,9 +1156,7 @@
  {
   "cell_type": "code",
   "execution_count": 90,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X_train_scaled, y_train, epochs=2,\n",
@@ -1856,9 +1852,7 @@
  {
   "cell_type": "code",
   "execution_count": 142,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"mse\", optimizer=\"nadam\")\n",
@@ -3527,9 +3521,7 @@
  {
   "cell_type": "code",
   "execution_count": 261,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([keras.layers.Dense(1, input_shape=[8])])\n",
@@ -3914,5 +3906,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
--- a/13_loading_and_preprocessing_data.ipynb
+++ b/13_loading_and_preprocessing_data.ipynb
@@ -466,9 +466,7 @@
  {
   "cell_type": "code",
   "execution_count": 26,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "n_inputs = 8 # X_train.shape[-1]\n",
@@ -2785,5 +2783,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/14_deep_computer_vision_with_cnns.ipynb
+++ b/14_deep_computer_vision_with_cnns.ipynb
@@ -1230,9 +1230,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "# Exercises"
   ]
@@ -1312,9 +1310,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "## 10.  Use transfer learning for large image classification"
   ]
@@ -1348,9 +1344,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "Simply open the Colab and follow its instructions."
   ]
@@ -1386,5 +1380,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/15_processing_sequences_using_rnns_and_cnns.ipynb
+++ b/15_processing_sequences_using_rnns_and_cnns.ipynb
@@ -972,9 +972,7 @@
  {
   "cell_type": "code",
   "execution_count": 52,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
@@ -1213,9 +1211,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "# Exercise solutions"
   ]
@@ -1974,9 +1970,7 @@
  {
   "cell_type": "code",
   "execution_count": 100,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "new_chorale_v2_hot = generate_chorale_v2(model, seed_chords, 56, temperature=1.5)\n",
--- a/16_nlp_with_rnns_and_attention.ipynb
+++ b/16_nlp_with_rnns_and_attention.ipynb
@@ -1248,5 +1248,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
--- a/extra_gradient_descent_comparison.ipynb
+++ b/extra_gradient_descent_comparison.ipynb
@@ -257,9 +257,9 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.7.6"
  }
 },
 "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
--- a/index.ipynb
+++ b/index.ipynb
@@ -48,9 +48,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "### To understand\n",
@@ -65,9 +63,7 @@
  {
   "cell_type": "code",
   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "outputs": [],
   "source": []
  }
@@ -88,7 +84,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.6"
  },
  "nav_menu": {},
  "toc": {
@@ -102,5 +98,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/math_linear_algebra.ipynb
+++ b/math_linear_algebra.ipynb
@@ -152,7 +152,10 @@
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
   },
   "outputs": [],
   "source": [
@@ -190,9 +193,7 @@
  {
   "cell_type": "code",
   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_vector2d(vector2d, origin=[0, 0], **options):\n",
@@ -232,9 +233,7 @@
  {
   "cell_type": "code",
   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1, 2, 8])\n",
@@ -1671,9 +1670,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "## Converting 1D arrays to 2D arrays in NumPy\n",
    "As we mentionned earlier, in NumPy (as opposed to Matlab, for example), 1D really means 1D: there is no such thing as a vertical 1D-array or a horizontal 1D-array. So you should not be surprised to see that transposing a 1D array does not do anything:"
@@ -2001,7 +1998,10 @@
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
   },
   "outputs": [],
   "source": [
@@ -2739,7 +2739,10 @@
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
   },
   "outputs": [],
   "source": [
@@ -3039,9 +3042,7 @@
  {
   "cell_type": "code",
   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "outputs": [],
   "source": []
  }
@@ -3062,7 +3063,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.7.6"
  },
  "toc": {
   "toc_cell": false,
@@ -3072,5 +3073,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/tools_matplotlib.ipynb
+++ b/tools_matplotlib.ipynb
@@ -554,9 +554,7 @@
  {
   "cell_type": "code",
   "execution_count": 24,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(x, x**2, px, py, \"ro\")\n",
@@ -1022,9 +1020,7 @@
  {
   "cell_type": "code",
   "execution_count": 42,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(img, cmap=\"hot\")\n",
@@ -1065,9 +1061,7 @@
  {
   "cell_type": "code",
   "execution_count": 44,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(img, interpolation=\"nearest\")\n",
@@ -1086,7 +1080,10 @@
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
   },
   "outputs": [],
   "source": [
@@ -1175,7 +1172,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
  },
  "toc": {
   "toc_cell": true,
@@ -1186,5 +1183,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/tools_numpy.ipynb
+++ b/tools_numpy.ipynb
@@ -21,9 +21,7 @@
  {
   "cell_type": "code",
   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
@@ -357,9 +355,7 @@
  {
   "cell_type": "code",
   "execution_count": 22,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
@@ -500,9 +496,7 @@
  {
   "cell_type": "code",
   "execution_count": 29,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "f = np.array([[1,2],[1000, 2000]], dtype=np.int32)\n",
@@ -663,9 +657,7 @@
  {
   "cell_type": "code",
   "execution_count": 38,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([14, 23, 32, 41])\n",
@@ -1243,9 +1235,7 @@
  {
   "cell_type": "code",
   "execution_count": 74,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
@@ -1542,9 +1532,7 @@
  {
   "cell_type": "code",
   "execution_count": 96,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "c[..., 3]  # all matrices, all rows, column 3.  This is equivalent to c[:, :, 3]"
@@ -2700,7 +2688,10 @@
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
   },
   "outputs": [],
   "source": [
@@ -2746,7 +2737,10 @@
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
   },
   "outputs": [],
   "source": [
@@ -2839,7 +2833,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
  },
  "toc": {
   "toc_cell": false,
@@ -2857,5 +2851,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
--- a/tools_pandas.ipynb
+++ b/tools_pandas.ipynb
@@ -1756,9 +1756,7 @@
  {
   "cell_type": "code",
   "execution_count": 97,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "grades >= 5"
@@ -1978,9 +1976,7 @@
  {
   "cell_type": "code",
   "execution_count": 110,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "bonus_points.interpolate(axis=1)"
@@ -2242,9 +2238,7 @@
  {
   "cell_type": "code",
   "execution_count": 125,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "much_data = np.fromfunction(lambda x,y: (x+y*y)%17*11, (10000, 26))\n",
@@ -2264,9 +2258,7 @@
  {
   "cell_type": "code",
   "execution_count": 126,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "large_df.head()"
@@ -2298,9 +2290,7 @@
  {
   "cell_type": "code",
   "execution_count": 128,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "large_df.info()"
@@ -2322,9 +2312,7 @@
  {
   "cell_type": "code",
   "execution_count": 129,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "large_df.describe()"
@@ -2775,9 +2763,7 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
   "source": [
    "# What next?\n",
    "As you probably noticed by now, pandas is quite a large library with *many* features. Although we went through the most important features, there is still a lot to discover. Probably the best way to learn more is to get your hands dirty with some real-life data. It is also a good idea to go through pandas' excellent [documentation](http://pandas.pydata.org/pandas-docs/stable/index.html), in particular the [Cookbook](http://pandas.pydata.org/pandas-docs/stable/cookbook.html)."
@@ -2807,7 +2793,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
  },
  "toc": {
   "toc_cell": false,
@@ -2818,5 +2804,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }