diff --git "a/Data Analitics/Week 4/.ipynb_checkpoints/TU257-Lab3-1-DataExploration-checkpoint.ipynb" "b/Data Analitics/Week 4/.ipynb_checkpoints/TU257-Lab3-1-DataExploration-checkpoint.ipynb" new file mode 100644--- /dev/null +++ "b/Data Analitics/Week 4/.ipynb_checkpoints/TU257-Lab3-1-DataExploration-checkpoint.ipynb" @@ -0,0 +1,1887 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "GgzCNBxVUuGo" + }, + "source": [ + "# Lab 2 - Sample solution\n", + "\n", + "This jupyter notebook contains a sample solution for Week 03 - Lab 02.\n", + "\n", + "To make sure you can run the entire notebook, check if you have the required libraries installed:\n", + "\n", + "```{pytnon}\n", + "pip install -U pandas matplotlib seaborn\n", + "```\n", + "\n", + "We first start by loading the required libraries. For this particular solution we need to load the `pandas` library:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "IqTcF3XBT3t0" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "75T1IiwjVFpb" + }, + "source": [ + "Now that we have the module loaded, let's read the csv dataset. To do that, we use the [`read_csv`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) function provided by the module. Note that we need to set the `sep` parameter to `','` given that is used to structure the file.\n", + "\n", + "***Tip***: whenever possible, check the csv file before trying to read it to check for the file structure and the types of separator is used." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "WxkHdhCQUF15" + }, + "outputs": [], + "source": [ + "df = pd.read_csv('/Users/brendan.tierney/Dropbox/4-Datasets/trainingset.csv', sep=',')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OSYYGia0V1JD" + }, + "source": [ + "Now, let's inspect the first few lines of the dataset to see what the data looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 27272 entries, 0 to 27271\n", + "Data columns (total 17 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 age 24570 non-null float64\n", + " 1 job 24314 non-null object \n", + " 2 marital 22849 non-null object \n", + " 3 education 20647 non-null object \n", + " 4 default 23798 non-null object \n", + " 5 balance 22510 non-null float64\n", + " 6 housing 25830 non-null object \n", + " 7 loan 25247 non-null object \n", + " 8 contact 23762 non-null object \n", + " 9 day 24364 non-null float64\n", + " 10 month 25496 non-null object \n", + " 11 duration 23545 non-null float64\n", + " 12 campaign 22815 non-null float64\n", + " 13 pdays 24138 non-null float64\n", + " 14 previous 24875 non-null float64\n", + " 15 poutcome 26817 non-null object \n", + " 16 y 27272 non-null object \n", + "dtypes: float64(7), object(10)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "id": "YbupMzsyVg5h", + "outputId": "f0175657-af5f-4169-ac69-87c72bce8da7" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomey
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" + ], + "text/plain": [ + " age job marital education default balance housing loan contact \\\n", + "0 44.0 JobCat9 NaN secondary no 29.0 yes no unknown \n", + "1 47.0 JobCat3 married unknown no 1506.0 yes NaN unknown \n", + "2 31.0 unknown single unknown no 1.0 no no unknown \n", + "3 26.0 JobCat6 single tertiary no 447.0 yes yes unknown \n", + "4 42.0 JobCat4 divorced tertiary NaN 2.0 yes no unknown \n", + "\n", + " day month duration campaign pdays previous poutcome y \n", + "0 5.0 may 0.0 1.0 -1.0 0.0 unknown TypeA \n", + "1 5.0 may 0.0 1.0 -1.0 0.0 unknown TypeA \n", + "2 5.0 may 0.0 1.0 -1.0 0.0 unknown TypeA \n", + "3 5.0 may 0.0 1.0 -1.0 0.0 unknown TypeA \n", + "4 5.0 may NaN 1.0 -1.0 0.0 unknown TypeA " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FLarE8DEWXg8" + }, + "source": [ + "## Data quality report\n", + "\n", + "Now we start building the data quality report for the dataset provided.\n", + "\n", + "`pandas` has a nice built-in function to generate such reports: the [`describe`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html) function. You have to call it using the same variable you stored the dataset after reading:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "id": "TXUk4YSkW112", + "outputId": "91da6113-f191-4d71-ae23-18e2d0076fb2" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age balance day duration campaign \\\n", + "count 24570.000000 22510.000000 24364.000000 23545.0 22815.000000 \n", + "mean 39.894302 1373.054687 15.779059 0.0 2.744773 \n", + "std 11.445373 3100.869406 8.295287 0.0 3.018258 \n", + "min 16.000000 -6847.000000 1.000000 0.0 1.000000 \n", + "25% 31.000000 71.000000 8.000000 0.0 1.000000 \n", + "50% 37.000000 443.000000 16.000000 0.0 2.000000 \n", + "75% 48.000000 1423.000000 21.000000 0.0 3.000000 \n", + "max 93.000000 98417.000000 31.000000 0.0 63.000000 \n", + "\n", + " pdays previous \n", + "count 24138.000000 24875.000000 \n", + "mean 39.980943 0.578251 \n", + "std 100.027931 1.943030 \n", + "min -1.000000 0.000000 \n", + "25% -1.000000 0.000000 \n", + "50% -1.000000 0.000000 \n", + "75% -1.000000 0.000000 \n", + "max 871.000000 58.000000 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Cgw1URu_W2mx" + }, + "source": [ + "Notice 2 things about the output: \n", + "\n", + "1. The generated report have the features on the columns and the statistics on the rows. When you have lots of features, that can make the visulization and comparison of the statistics difficult.\n", + "2. The generated report only contains the continuous features.\n", + "\n", + "\n", + "Let's first fix the position of features and the statistics. To do that, we must add a new function call after the `describe` function: the `transpose` function. This second function call will transpose the rows and columns of the report so it will much easier to visualize the statistics when we have dozen of features in our dataset.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 266 + }, + "id": "gGFn6AGZVhtX", + "outputId": "d19c1ea6-9b23-4f19-9b99-a983018a6fa6" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
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" + ], + "text/plain": [ + " count mean std min 25% 50% 75% \\\n", + "age 24570.0 39.894302 11.445373 16.0 31.0 37.0 48.0 \n", + "balance 22510.0 1373.054687 3100.869406 -6847.0 71.0 443.0 1423.0 \n", + "day 24364.0 15.779059 8.295287 1.0 8.0 16.0 21.0 \n", + "duration 23545.0 0.000000 0.000000 0.0 0.0 0.0 0.0 \n", + "campaign 22815.0 2.744773 3.018258 1.0 1.0 2.0 3.0 \n", + "pdays 24138.0 39.980943 100.027931 -1.0 -1.0 -1.0 -1.0 \n", + "previous 24875.0 0.578251 1.943030 0.0 0.0 0.0 0.0 \n", + "\n", + " max \n", + "age 93.0 \n", + "balance 98417.0 \n", + "day 31.0 \n", + "duration 0.0 \n", + "campaign 63.0 \n", + "pdays 871.0 \n", + "previous 58.0 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe().transpose()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SkWmJsrXYL5P" + }, + "source": [ + "Notice how it looks much nicer now.\n", + "\n", + "Let's now fix the categorical features. By default, `pandas` runs the describe function for numerical values given that the rport for continuous and categorical features are different.\n", + "\n", + "To generate a report for categorical features, we must tell the `describe`function what types of features we want to include in the report, we have to pass the parameter `include` to the function and inform which fatures we want. In `pandas`, categorical features are defined either as python `objects` or pandas `category` type. Let's generate our report for the categorical features and transpose the columns of the report at the same time for an easier visualization." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 359 + }, + "id": "b-tnZbztWTkd", + "outputId": "a07191ee-474a-4aa2-fb4e-1007dffaf7c3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countuniquetopfreq
job2431412JobCat35298
marital228493married13782
education206474secondary10573
default237982no23368
housing258302yes14336
loan252472no21228
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month2549612may7758
poutcome268174unknown21951
y272722TypeA24030
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" + ], + "text/plain": [ + " count unique top freq\n", + "job 24314 12 JobCat3 5298\n", + "marital 22849 3 married 13782\n", + "education 20647 4 secondary 10573\n", + "default 23798 2 no 23368\n", + "housing 25830 2 yes 14336\n", + "loan 25247 2 no 21228\n", + "contact 23762 3 cellular 15300\n", + "month 25496 12 may 7758\n", + "poutcome 26817 4 unknown 21951\n", + "y 27272 2 TypeA 24030" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe(include=['object', 'category']).transpose()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rZnajWixagCE" + }, + "source": [ + "## Visualizations\n", + "\n", + "Now that we generated the data quality report, let's generate the visualizations for the distributions of each feature.\n", + "\n", + "For that, we will use another `pandas`' built-in function: the `plot` function. When calling that function, we must pass the parameter `kind` to inform what type of plot we wish to display.\n", + "\n", + "In addition, as we are ploting individual features, we must first select the feature we want to visualize.\n", + "\n", + "***Tip***: set the `title` parameter of the plot function to make it easier to find particular plots in your notebook.\n", + "\n", + "\n", + "> #### Continuous features - histograms \n", + "> For visualizing continuous features, we use histograms. To inform pandas to generate an histogram, we set the parameter `kind='hist'`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 298 + }, + "id": "TOAS9i_ZafMz", + "outputId": "378781be-269c-4820-aae0-fc9070fb0979" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['previous'].plot(kind='hist', title='previous')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lapa4Ff3cg71" + }, + "source": [ + "> #### Categorical features - bar plots \n", + "> For visualizing categorical features, we use bar plots. To inform pandas to generate a bar plot, we set the parameter `kind='bar'`. However, that is not enough for the `plot` function as it doesn't know how to plot categories. To be able to generate such plots, we must generate the counts (frequency) of each category and provide those counts to the plot function. Fortunately, there is a buil-in function in pandas that does exactly that for us: `value_counts`.\n", + "> \n", + "> To use it, we first select the feature we want to plot and then we call the `value_counts` function. Finally, we call the plot function, cahining all the three together." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 334 + }, + "id": "HOYb7FkEbwLG", + "outputId": "fe375a5e-87e7-46a0-a1c8-fdf3ceb63e1c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['month'].value_counts().plot(kind='bar', title='month')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 334 + }, + "id": "3xJLvm0Ad3-G", + "outputId": "46e127e7-22bb-4168-c829-be95c2ceef7b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['poutcome'].value_counts().plot(kind='bar', title='poutcome')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XFhqUr9LeWSb" + }, + "source": [ + "> #### Target features \n", + "> Let's not forget the target feature. Given that it is a categorical feature, we'll use a bar plot." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 318 + }, + "id": "HEKGqnGXeiZ2", + "outputId": "ee815d6a-bcde-4555-9ff6-ba336257b1c6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['y'].value_counts().plot(kind='bar', title='target feature')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "soYbhq1_eob_" + }, + "source": [ + "## Bonus 1 - Ploting several features at once\n", + "\n", + "Let's now see how we can visualize several features at once using `pandas` and the `matplotlib` [docs](https://matplotlib.org/contents.html) modules.\n", + "\n", + "For ploting several features at once, we will create a `figure` using matplotlib built-in function `subplots`. The `figure` object will hold the invidual plots. Therefore, when we create that object, we must inform how many rows and how many columns that figure will contain. In addition, we have to define the size of the figure using the `figsize` parameter. \n", + "\n", + "This is a sample of how the code will look like:\n", + "\n", + "```{python} \n", + "fig, axes = plt.subplots(nrows=3,ncols=3, figsize=(18, 12))\n", + "```\n", + "\n", + "The `fig` variable contains the figure itself. The `axes` variables contains an object that represent the positions and a reference for each plots within the figure. The reference contained in `axes` is a coordinate system that we can reference using the syntax:\n", + "\n", + "```{python}\n", + "[row_index, column_index]\n", + "```\n", + "\n", + "***Tip***: sometimes it can be hard to identify separations of values of features in plots generated with `matplotlib` . It is also difficult to identify the values for frequency as well. To help this out, we'll use a python module called `seaborn` [docs](https://seaborn.pydata.org/). To make the plots look a bit nicer we must import the `seaborn` module and call the built-in function `set`. That will change the default look and feel of the plots, including plots generated with `pandas`.\n", + "\n", + "***Tip 2:*** put the code for setting `seaborn` look and feel at the top of the jupyter notebook to change all `matplotlib` plots.\n", + "\n", + "***Tip 3:*** once the plots are in place, use the function `tight_layout` to fit all the plots in the image and organize the titles, labels and legends." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "bjqmDjt7WH78" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XIy_f9O3kudE" + }, + "source": [ + "### Continuous features - histograms\n", + "\n", + "There are 7 continuous features in our datasets. Let's create a figure with 3 columns to plot our features. That way, we need to create a figure with 3 rows of plots. This will leave us with 9 positions in the figure: \n", + "\n", + "$3\\text{ rows}\\times3\\text{ columns}=9 \\text{ positions}$\n", + "\n", + "Once we have the coordinates, we inform the position of each plot in the `ax` parameter of the plot `function`.\n", + "\n", + "Now that we have the figure and the reference to the axes, lets generate all plots at once. When we do that, we must tell the `plot` function to position the plot in a particular coordinate of the figure. Once we plot all features, we can proceed to remove the 'empty' plots (there will be 2 empty plots as we have 9 positions and only 7 features).\n", + "\n", + "For more details see the comments in the code below." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 869 + }, + "id": "v-2GKH_GXCM3", + "outputId": "57efd60a-4b06-4c49-ee73-dbc215074241" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# let's create the figure and axes wit 3 rows anc 3 columns\n", + "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(18, 12))\n", + "\n", + "# now we create the plots, informing the coordinate\n", + "df['age'].plot(kind='hist', ax=axes[0, 0], title='age') # position [0, 0]\n", + "df['balance'].plot(kind='hist', ax=axes[0, 1], title='balance') # position [0, 1]\n", + "df['day'].plot(kind='hist', ax=axes[0, 2], title='day') # position [0, 2]\n", + "df['duration'].plot(kind='hist', ax=axes[1, 0], title='duration') # position [1, 0]\n", + "df['campaign'].plot(kind='hist', ax=axes[1, 1], title='campaign') # position [1, 1]\n", + "df['pdays'].plot(kind='hist', ax=axes[1, 2], title='pdays') # position [1, 2]\n", + "df['previous'].plot(kind='hist', ax=axes[2, 0], title='previous') # position [2, 0]\n", + "\n", + "# now we remove the plots\n", + "fig.delaxes(axes[2, 1]) # position [2, 1]\n", + "fig.delaxes(axes[2, 2]) # position [2, 2]\n", + "\n", + "# here we tidy the figure layout\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aAWZWLhvq5DC" + }, + "source": [ + "### Categorical features - bar plots\n", + "\n", + "There are 9 categorical features in our datasets. Let's create a figure with 3 columns to plot our features. That way, we need once again to create a figure with 3 rows of plots. This will leave us with 9 positions in the figure: \n", + "\n", + "$3\\text{ rows}\\times3\\text{ columns}=9 \\text{ positions}$\n", + "\n", + "There will be no empty plots in the figure. The rest of the process is the same as for categorical features. For more details see the comments in the code below." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 869 + }, + "id": "fnKKEe8rcX4K", + "outputId": "4f2cb5b8-3477-4e2d-873c-d98dbf21373b" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# let's create the figure and axes wit 3 rows anc 3 columns\n", + "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(18, 12))\n", + "\n", + "# now we create the plots, informing the coordinate - notice that we have to use\n", + "# the value_couts function because we're ploting categorical features\n", + "df['job'].value_counts().plot(kind='bar', ax=axes[0, 0], title='job') # position [0, 0]\n", + "df['marital'].value_counts().plot(kind='bar', ax=axes[0, 1], title='marital') # position [0, 1]\n", + "df['education'].value_counts().plot(kind='bar', ax=axes[0, 2], title='education') # position [0, 2]\n", + "df['default'].value_counts().plot(kind='bar', ax=axes[1, 0], title='default') # position [1, 0]\n", + "df['housing'].value_counts().plot(kind='bar', ax=axes[1, 1], title='housing')# position [1, 1]\n", + "df['loan'].value_counts().plot(kind='bar', ax=axes[1, 2], title='loan') # position [1, 2]\n", + "df['contact'].value_counts().plot(kind='bar', ax=axes[2, 0], title='contact') # position [2, 0]\n", + "df['month'].value_counts().plot(kind='bar', ax=axes[2, 1], title='month') # position [2, 1]\n", + "df['poutcome'].value_counts().plot(kind='bar', ax=axes[2, 2], title='poutcome') # position [2, 2]\n", + "\n", + "# here we tidy the figure layout\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "id": "od-1xf__dPbK", + "outputId": "b39af2c4-c1ba-4d8a-c12d-12332c5bc1b6" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['y'].value_counts().plot(kind='bar', title='y')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "22nkCfTbFiBB" + }, + "source": [ + "### Bonus 2 - Plot features using a foor loop\n", + "\n", + "First we must separete the column into lists of their types:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "UrsF_S-9BtHp" + }, + "outputs": [], + "source": [ + "categorical_columns = ['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'poutcome']\n", + "numerical_columns = ['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7_MO8DPiN6Iz" + }, + "source": [ + "#### Categorical plots" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 873 + }, + "id": "2U9HsQqUAmd0", + "outputId": "da641a0c-0d0d-4c17-dd11-a36ce3d75137" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_cols = 3 # define the number of columns\n", + "\n", + "# now we check if the number of columns of that type is divisible by the number\n", + "# of columns we defined\n", + "if len(categorical_columns) % n_cols == 0: # if it is devisible (no remainder)\n", + " n_rows = int(len(categorical_columns) / n_cols)\n", + "else: # if it is not devisible (no remainder)\n", + " n_rows = int(len(categorical_columns) / n_cols) + 1\n", + "\n", + "# define the figure and its subplots\n", + "fig, axes = plt.subplots(nrows=n_rows,ncols=n_cols, figsize=(18, 12))\n", + "\n", + "cur_col = 0 # here we'll store the current column of the plot we are at\n", + "cur_row = 0 # here we'll store the current row of the plot we are at\n", + "\n", + "for column_name in categorical_columns: # iterate over the column's names\n", + " # define the plot passing current row and column to the axes param of the plot\n", + " df[column_name].value_counts().plot(kind='bar', ax=axes[cur_row, cur_col], title=column_name)\n", + " # once we add a new plot, we must move one column to the right, so we add 1 to\n", + " # the current count of columns\n", + " cur_col += 1\n", + " # after, we must check we are still within the boundaries (remember that python\n", + " # start indexing at 0)\n", + " if cur_col == n_cols: # if we are beyond the boundary:\n", + " cur_row += 1 # move to the next row (i.e., increment the row count)\n", + " cur_col = 0 # reset the count of columns (i.e., position at the first column)\n", + "\n", + "# remove unused axes\n", + "for ax in axes.flat[len(numerical_columns):]:\n", + " ax.remove() \n", + "\n", + "# clean layout \n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f1tkGZXZN_Nk" + }, + "source": [ + "#### Numerical plots" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 873 + }, + "id": "dHZqhsQJBfpo", + "outputId": "94834c97-0d6c-4787-ffe2-2a1da90dc365" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_cols = 3 # define the number of columns\n", + "\n", + "# now we check if the number of columns of that type is divisible by the number\n", + "# of columns we defined\n", + "if len(numerical_columns) % n_cols == 0: # if it is devisible (no remainder)\n", + " n_rows = int(len(numerical_columns) / n_cols)\n", + "else: # if it is not devisible (no remainder)\n", + " n_rows = int(len(numerical_columns) / n_cols) + 1\n", + "\n", + "# define the figure and its subplots\n", + "fig, axes = plt.subplots(nrows=n_rows,ncols=n_cols, figsize=(18, 12))\n", + "\n", + "cur_col = 0 # here we'll store the current column of the plot we are at\n", + "cur_row = 0 # here we'll store the current row of the plot we are at\n", + "\n", + "for column_name in numerical_columns: # iterate over the column's names\n", + " # define the plot passing current row and column to the axes param of the plot\n", + " df[column_name].plot(kind='hist', ax=axes[cur_row, cur_col], title=column_name)\n", + " # once we add a new plot, we must move one column to the right, so we add 1 to\n", + " # the current count of columns\n", + " cur_col += 1\n", + " # after, we must check we are still within the boundaries (remember that python\n", + " # start indexing at 0)\n", + " if cur_col == n_cols: # if we are beyond the boundary:\n", + " cur_row += 1 # move to the next row (i.e., increment the row count)\n", + " cur_col = 0 # reset the count of columns (i.e., position at the first column)\n", + "\n", + "# remove unused axes\n", + "for ax in axes.flat[len(numerical_columns):]:\n", + " ax.remove() \n", + "\n", + "# clean layout \n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "suaadSYhuGGF" + }, + "source": [ + "## Challenge\n", + "\n", + "Choose one of the two methods above (Bonus 1 or Bonus 2) and try to combine the plots for all features (continuous and categoricals) in a single plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vpFiQzzcuTM5" + }, + "outputs": [], + "source": [ + "# write your code here" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "Lab02-PythonSolution", + "provenance": [] + }, + "interpreter": { + "hash": "f2fb7fda8d2754165ee53020f70d8739bfae2298bc6c1a05d385a37b77f23af3" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}