{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"tags": [
"remove_input"
]
},
"outputs": [],
"source": [
"path_data = '../../data/'\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('bmh')\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualizing Categorical Distributions\n",
"\n",
"Data come in many forms that are not numerical. Data can be pieces of music, or places on a map. They can also be categories into which you can place individuals. Here are some examples of *categorical* variables.\n",
"\n",
"- The individuals are cartons of ice-cream, and the variable is the flavor in the carton.\n",
"- The individuals are professional basketball players, and the variable is the player's team.\n",
"- The individuals are years, and the variable is the genre of the highest grossing movie of the year.\n",
"- The individuals are survey respondents, and the variable is the response they choose from among \"Not at all satisfied,\" \"Somewhat satisfied,\" and \"Very satisfied.\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The DataFrame `icecream` contains data on 30 cartons of ice-cream. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
Flavor
\n",
"
Number of Cartons
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
Chocolate
\n",
"
16
\n",
"
\n",
"
\n",
"
1
\n",
"
Strawberry
\n",
"
5
\n",
"
\n",
"
\n",
"
2
\n",
"
Vanilla
\n",
"
9
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Flavor Number of Cartons\n",
"0 Chocolate 16\n",
"1 Strawberry 5\n",
"2 Vanilla 9"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"icecream = pd.DataFrame({\n",
" 'Flavor':np.array(['Chocolate', 'Strawberry', 'Vanilla']),\n",
" 'Number of Cartons':np.array([16, 5, 9])}\n",
")\n",
"icecream"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The *values* of the **categorical** variable \"flavor\" are chocolate, strawberry, and vanilla. The df shows the number of cartons of each flavor. We call this a *distribution table*. A *distribution* shows all the values of a variable, along with the frequency of each one."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bar Chart (pandas)\n",
"The bar chart (*as opposed to Histogram*) is a familiar way of visualizing categorical distributions. It displays a bar for each category. The bars are equally spaced and equally wide. The length of each bar is proportional to the frequency of the corresponding category.\n",
"\n",
"We will draw bar charts with horizontal bars because it's easier to label the bars that way. The *pandas* df method is therefore called `barh`. It takes two arguments: the first is the column label of the categories, and the second is the column label of the frequencies."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"icecream.plot.barh('Flavor', 'Number of Cartons')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the table consists just of a column of categories and a column of frequencies, as in `icecream`, the method call is even simpler. You can just specify the column containing the categories, and `barh` will use the values in the other column as frequencies."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"icecream.plot.barh('Flavor')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Features of Categorical Distributions\n",
"Apart from purely visual differences, there is an important fundamental distinction between bar charts and the two graphs that we saw in the previous sections. Those were the scatter plot and the line plot, both of which display two numerical variables – the variables on both axes are numerical. In contrast, the bar chart has categories on one axis and numerical frequencies on the other.\n",
"\n",
"This has consequences for the chart. First, the width of each bar and the space between consecutive bars is entirely up to the person who is producing the graph, or to the program being used to produce it. Python made those choices for us. If you were to draw the bar graph by hand, you could make completely different choices and still have a perfectly correct bar graph, provided you drew all the bars with the same width and kept all the spaces the same.\n",
"\n",
"Most importantly, the bars can be drawn in any order. The categories \"chocolate,\" \"vanilla,\" and \"strawberry\" have no universal rank order, unlike for example the numbers 5, 7, and 10.\n",
"\n",
"This means that we can draw a bar chart that is easier to interpret, by rearranging the bars in decreasing order. To do this, we first rearrange the rows of `icecream` in decreasing order of `Number of Cartons`, and then draw the bar chart."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAD1CAYAAAAF1WFdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAto0lEQVR4nO3de3hddZno8e+b7KRJc2vThKQKp1UuYgEplA7YUigURmSwMkcUZgYFBnGOB0V9dBxF7SmOAo/j4+ioyCBqPQ8OMGLlJh7ulwIK2HIpUKAgLa20pU3a3NNkJ+/5IzshDdm7zfqt1fXu8n6ep0+SvXdWvjs73b+9Lr+9RFVxzjnnrClJO8A555wbjw9QzjnnTPIByjnnnEk+QDnnnDPJByjnnHMm+QDlnHPOpEzaAfuSBx54QCdNmpR2hnPOFY3u7u5tixYtahzvOh+gYnbooYemnTCu9evXM2PGjLQz8vK+MN4XxvuiC21btWrV+nzX+Sa+GIlI2gl5lZWVpZ1QkPeF8b4w3hddkm0+QMWotLQ07YS86urq0k4oyPvCeF8Y74suyTYfoGKUzWbTTshr27ZtaScU5H1hvC+M90WXZJvvg4qRr0FF531hrPfV1tbS0dGB1ff+rKqqor29Pe2MvCz37WmbiFBdXT2hXSE+QMXI6n8+gL6+vrQTCvK+MNb7ent7mTZtGuXl5WmnjKu/v9/0fh7LfXva1tfXR2dnJzU1NXu8bN/EF6PBwcG0E/Lq6elJO6Eg7wtjvU9VzQ5OYPv/Ltju29O28vLyCb+I9wEqRlZf4QA0NzennVCQ94Wx3ldZWZl2QkGW/++C7T4/iq9I9Pf3p52Q1+bNm9NOKMj7wljv6+3tTTuB+vp6vv71r498/cMf/pArr7wSCP+/e/HFF3PLLbcELaOQ4b6bb76ZY489lsWLF7/lNi+//DIf+9jHmDNnDsceeywXXHABb7zxxh7/jLa2Nn72s59FbkuC74OKUUmJ3fHe8uYV8L5Q1vvG7hj/62ufjHX5d33yqN3eZtKkSdx+++184QtfYNq0aQX79qaBgYHdHmA13Hfdddfx3e9+lwULFuxyfW9vL+eccw7f+ta3OO200wBYsWIF27ZtY7/99tujhuEB6sILL5xQf5K/O7vPqEXI8gA1kR2TafC+MNb7LGyiymQynHfeefzkJz95y3Wf+9zndlkDOuCAAwB4+OGHOeOMM7jggguYO3cul112Gb/+9a855ZRTmD9/Pq+++urI9zz44IOcfvrpzJ07lzvvvBMYeuJfsmQJixYt4vjjj2fZsmUjy128eDEXXXQR8+fPf0vPb37zG+bPn8+8efNYunQppaWlfOc73+Gxxx7ji1/8IkuWLNnl9jfddBNz584dGZwAFixYwKxZs3jttdc4/fTTWbhwIQsXLuSxxx4bt+Gyyy5j3bp1nHDCCSxZsgRVZcmSJcybN4/58+ezfPnyke/70Ic+xHnnncexxx7LxRdfPLJv6bLLLuO4447j+OOP5xvf+MaEH6OxfA0qRpbnQbW0tFBdXZ12Rl7eF8Z6n5WjDC+88EIWLFjAJZdcssvlhXbeP/vss/zxj39k6tSpHH300Zx77rncc889XH311VxzzTVcccUVALz22mvcfvvtvPrqqyxevJiVK1dyww03UFtby7333svOnTv54Ac/yEknnQTAqlWreOSRR97yNkGbNm1i6dKl3H///UyZMoWPfOQj3HbbbXz5y19mxYoVfPOb3+Soo3ZdY1yzZg1HHnnkuP0NDQ0sX76ciooKXnnlFS666CLuu+++tzS89tprrFmzhoceegiAW2+9ldWrV7NixQpaWlpYtGgR8+bNA+CZZ57h0UcfZfr06XzgAx/gscce4z3veQ+/+93veOyxxxAR2tra9vRhycvuS/4ilMnYHe+nTp2adkJB3hfGep+VTZC1tbWcffbZ/Od//uculxfaTHXUUUfR3NzMpEmTmDlz5sgAM2vWLDZs2DByuzPPPJOSkhIOPPBAZs6cydq1a7n//vu58cYbOeGEEzj11FNpbW3llVdeAeDoo48e9z3snnzySY4//ngaGhrIZDKcddZZPP7445Hvczab5fOf/zzz58/nggsu4MUXXxy5Ll8DwB//+Ec+8pGPUFpayn777cf8+fN58sknR77vne98JyUlJRxxxBG89tpr1NTUMGnSJC655BJuu+22WA6M8QEqRpYPBbV+GLL3hbHeNzAwkHbCiE9/+tNcd911dHd3j1xWWlo68v9XVXdZ4xt9hoKSkpKRr0tKSnbZajJ2kBMRVJUrr7yShx56iIceeoinnnqKk08+GYDJkyeP2zfe2tzuDs8+9NBDefrpp8e97qqrrqKxsZEVK1Zw33337XLf8jXs7meO/Z1ks1kymQz33HMPixcv5o477uCjH/1oweY94QNUjCwPUBaOoirE+8JY77M0QE2dOpUzzzyT6667buSy/ffff+QJ/o477oh0ZNott9zC4OAgr776KuvWreOggw7i5JNP5he/+MXI8l5++WW6uroKLmfOnDk88sgjtLS0MDAwwPLlyznuuOMKfs/wWtZdd901ctk999zD888/T3t7O01NTZSUlHDjjTfmfSyqq6vp7Owc+XrevHn89re/ZWBggG3btvHoo49y9NFHv+X7hgeyzs5O2tvbOfXUU7n88stZvXp1weY9YXebVBGysCM4H+vzZLwvjPW+ioqKtBN2cfHFF3PttdeOfH3++efz8Y9/nFNOOYUTTjiBqqqqCS/zoIMO4owzzmDr1q1873vfo6Kigk984hNs2LCBhQsXoqo0NDTsMjCOp7m5mSVLlrB48WJUlVNPPZUPfehDBb+nsrKS66+/nksvvZRLL72UTCbDYYcdxhVXXMGFF17Ieeedxy233MKCBQvy3rf6+nqOPfZY5s2bxymnnMJll13GE088wYIFCxARli5dSlNTE2vXrt3l+4YPDuvs7OTcc8+lt7cXVeXb3/72BH574xPLb89TbB544AHNt6MybZbPJwPeF8p636ZNm5g+fXraGXnt3LkTyycbtdw3kbb29nZqa2t3uWzVqlUrFy1adMx4t/c1qBi90ZWNfX5HXI6s6+fpu1vTzsgr7r49mRczEdbWAMay3mf5jZTB9hQRsN2XZJvde12EBgyvjLb02X6orfdZf6se630+QIWx3OcDVJGYVGJ3hDqwys5O6vFY79u+fXvaCQVZ77MyDyofy3MYwXZfkm0+QMWod9DuKd9f6rD9CtZ639i3xrHGep+VeVD5WJ7DCLb7kmzzASpGZYZ/m9Mr7R4CD/b7Ojo60k4oyHrfzp07Ta9FWToMfjyW+/a0ra+vb8Lv22d3WC5CpdjdxFeTsdsG9vssP7lCcfTt3LnT7Hytrq6uSIeW7y2W+/a0bfiMuhPhA1SMugbsbuJbucP2Q229z/o8I+t906dPN3uYNAy9M4L3RZNkm+GNUsWnqtTuWsCcKXZ3soL9PuvnW/K+MN4XXZJtPkDFKKt216CsH8Ztvc/6YdzeF8b7okuyzfazQpGxPA+qI2t38AT7fdaPQvO+MN4XXZJtPkDFyPI8qJmT7R4FBPb74ji3TZK8L4z3RZdkmw9QMeoxPA9qTYftgxCs9zU0NKSdUJD3hfG+6JJs8wEqRpMM/zZnGF9Dsd5n+RUseF8o74vO16CKRInheVCTDR9hCPb7opwfaG/yvjDeF12SbXttgBKRr4nIcyLyjIg8JSLHisjnRST/KR2j/6xlInJW3MvdHZ8HFZ31PuvzjLwvjPdFl2TbXhmgROT9wBnA0ar6PuAUYAPweWDcAUpEUnlztrE/dyIdPg8qOut9luehgPeF8r7o9oV5UNOBbaq6E0BVtwFnAe8A7heR+wFEpFNEvikijwHvF5ElIvKEiDwrItfIkP1EZGXu9keKiIrI/8h9/cqoNbJTRGSFiLwkImfkri8VkX/LLfMZEfmn3OULReR+EfkvYPU4X/+riHxu+M6IyLdF5JKxd7Lf8DyoLTttb8213mf1bWaGeV8Y74suyba99axwF3BAbrC4SkROVNX/AF4HTlLVk3K3qwKeVdVjVfVh4EeqOldVDwcqgTNU9Q2gQkRqgQXAn4AFIjIDeENVu3PLmgmcCPwNcLWIVAAXAm2qOheYC1wkIu/K3f6vgK+p6qxxvv4ZcB6AiJQA5wC/Gnsn7a4/Qb/t92I132f9fEbeF8b7okuyba9s+FfVThGZw9CAchJwo4h8ZZybDgC/GfX1SSLyZYY2A9YDzwG3AY8C84ETgMuB0wABVoz63v9W1UFgrYj8GTgU+GvgfaP2T9UBBwN9wOOq+uqo7x/5WlXXiUiLiBwFNAFPqmrL2PjBrh1s+PGl9AwOHdE3+8TT6J+9mDlTsmzZWUL/IOxfOciz7RkOrh4gI8rq9gyz67Js6h16rTC9YpCn2jIcUZslq8LazlIOr82ysaeEshJomjTIyh0Z5kzJ0j0grO8u5b01WdZ1l1KTUaaVv3l9R1bY1FPCITUD1GYGqc4oU8p05Pod/UJLXwkHVg3wUkcp0ysHqcm8eX1LXwkdWWHm5AHWdGSYMXmAyaVvXh/nfSovUfavHJzQfXqlq5Rp5YPj3qf169czbdo0Ojo66Ovro7m5mc2bN1NZWUl5eTltbW00NDTQ1tZGf3//yPVVVVWUlpbS3t5OY2Mjra2tqCp9fX20t7ePvNllZ2cnTU1NbN26FRGhvr6erVu3Ultby8DAAF1dXSPLLCsro66ujm3btlFXV0dfXx89PT0j15eXl1NTU0NLSwtTp06lp6eH3t7ekesrKiqorKxk+/btee/T9u3baW9vn9B9amxsZMuWLXvlPm3atIn29vYJ3acoj1PU+7RhwwZ6enoSf5yi3qcNGzYwODi4z/7t5SOqe/91f26AOA84Ajgmt8kPEelU1erc5xXA+tz1G0RkKYCqLhWRTzA04CwC3s/QgPUUcLuq3i4iy4AHVfUXuWU9BHwWWAJco6p3julZCHxJVc8Y7+vcZWcD84Bm4JeqesfY+/Wr2+/R76+xualqWvmg6bcTirsv7lO+d3d3M3ly7MfzxMb7wnhfdKFtq1atWrlo0aJjxrtubx0k8R4ROXjURbMZGnw6gJo831aR+7hNRKoZ2mc17CHgXGBtbi2pFTgdeGTUbT4qIiUiciDwbuBF4E7g0yJSlus6RET2dAPqbxlaU5ubW85bWJ4HdXC17XlG1vtaW1vTTijI+8J4X3RJtu2tY3urgR+KyBQgC7wMfAr4O+D3IrJp1H4oAFR1h4j8FFgNrAOeGHXdutyJrx7KXfQwsL+qjj7v9YvAgwxtkvtfqtorItcytG9qlQwtYCtw5p7cAVXtyx3MsUNVx302FcN7oTJitw3s96WxpWEivC+M90WXZFsqm/iKUe7giFXAR1V17Xi3ue62e/QHL9hcjaorG6St32YbxN8X9ya+3t5eKioqdn/DlHhfGO+LLrQt9U18xU5EZjG01ndvvsEJbL8bwuw62/OMrPdt2bIl7YSCvC+M90WXZJvt6ftGqOrzDO3HKsjyPKjhI+qsst430VNV723eF8b7okuyzfazgnPOubctH6BiVGZ4R//0CtszYa33dXZ2pp1QkPeF8b7okmzzASpG3YbfLPapNttbc633NTU1pZ1QkPeF8b7okmzzASpGlYYPkjii1vZBCNb7tm7dmnZCQd4XxvuiS7LNB6gYKXbXoLKGD+AA+325eXdmeV8Y74suyTYfoGK00/BulLWddt9sEuz31dfXp51QkPeF8b7okmzzASpGlSV2N/EdbnwTmvU+y5tYwPtCeV90vomvSPQZ3ky1scf2Q229r7a2Nu2EgrwvjPdFl2Sb7WeFImN3eIIy44+09b6BAdtvZut9YbwvuiTbjD8tFBfL86CaJhneQYb9vq6urrQTCvK+MN4XXZJtPkDFqMvwPKiVO2zPM7Le19zcnHZCQd4XxvuiS7LN9rNCkTmgJsNdnzwy7YxxrV+/nhkzZqSdkZf1vs2bN3tfAO8LY7kvyTZfg4qR5bkKZWVlaScU5H1hvC+M90WXZJsPUDEqLbU7l6euri7thIK8L4z3hfG+6JJs8wEqRtms3bk827ZtSzuhIO8L431hvC+6JNt8gIqRr0FF531hvC+M90Xna1BFQtXuYeZ9fX1pJxTkfWG8L4z3RZdkmw9QMRoctDuXp6enJ+2EgrwvjPeF8b7okmzzASpGlo+0sTyPArwvlPeF8b7okmzzASpG/f39aSfktXnz5rQTCvK+MN4XxvuiS7LNB6gYlZTY/XWWl5ennVCQ94XxvjDeF12SbXafUYuQ5QGqpqYm7YSCvC+M94XxvuiSbLP7jFqELM+DamlpSTuhIO8L431hvC+6JNt8gIpRJmP3rQ2nTp2adkJB3hfG+8J4X3RJtvkAFSM/zDw67wvjfWG8Lzo/zLxIWB6gent7004oyPvCeF8Y74suyTYfoGLk86Ci874w3hfG+6LzeVBFwudBRed9YbwvjPdF5/OgioTlw8wrKirSTijI+8J4Xxjviy7JNrvPqEXI8gBVWVmZdkJB3hfG+8J4X3RJttl9Ri1CludBbd++Pe2EgrwvjPeF8b7okmzzASpGludBTZs2Le2EgrwvjPeF8b7okmzzASpGlg8z7+joSDuhIO8L431hvC+6JNt8gIqR5QHK8gnPwPtCeV8Y74vOT1hYJHweVHTeF8b7wnhfdD4Pqkj4PKjovC+M94Xxvuh8HlSR8MPMo/O+MN4Xxvui88PMi4SIpJ2Ql+UTnoH3hfK+MN4XnZ+wsEgMDAyknZBXW1tb2gkFeV8Y7wvjfdEl2eYDVIwsz4NqaGhIO6Eg7wvjfWG8L7ok23yAipGvQUXnfWG8L4z3RedrUEVCVdNOyMvyEYbgfaG8L4z3RZdkmw9QMfJ5UNF5XxjvC+N90fk8qCJh+VWO5XkU4H2hvC+M90Xn86CKRGlpadoJeVVVVaWdUJD3hfG+MN4XXZJtPkC9TVgePMH7QnlfGO+LLsk2H6BiZPkovvb29rQTCvK+MN4XxvuiS7LN7sSdIvSXziz/fO2TaWeMa1r5IC19rWln5OV9YeLuu+uTR8W2LIDGxsZYlxc374suyTZfg4rRJMO/zYOr7a7dgfeFst7X2mp3cAfvC5Fk2x4/pYqI4adfGwS786AyYrcNvC+U9T7LcwTB+0Ik2bZHg46IlAJdIjIpsZJ9QM+A3TeLXd1ue2uu94Wx3md5ExV4X4jUN/Gp6gDwEpDcyef3AZNL7b7KmV2XTTuhIO8LY71vy5YtaScU5H3RJdk2kZddvwJuF5EfABvhze1Zqnpf3GHFqF/trkFt6rW9hdb7wljvq66uTjuhIO+LLsm2iQxQn859XDrmcgXeHUuNc845l7PHL7tU9V15/vnglFNmeEf19IrBtBMK8r4w1vs6OzvTTijI+6JLsm1Ce1ZFJAPMA97J0Ga+P6iq7Y3fe1G34YMknmqzvRPd+8JY72tqako7oSDviy7JtokcZn4osAb4L+AS4HrgBRF5b0JtRafS8EESR9Tafh3hfWGs923dujXthIK8L7ok2yayZ/Uq4BrgAFV9v6ruD1ydu9wBit01qKzhAzjA+0JZ7xPxvhCW+5Jsm8gANRv4nu46K+v7ucsdsNPwboC1nXbfbBK8L5T1vvr6+rQTCvK+6JJsm8gA9Tpw4pjLFuQud0Blid1NfIcb3wTkfWGs91neRAXeFyLJtonsWb0UuFVEbgfWAzOAvwHOTSKsGPUZ3syyscf2PBnvC2O9r7a2Nu2EgrwvuiTbJnKY+a3A0cCzQE3u4xxVvSWhtqJjd3iCMtvPX94XyHqf5VPRgPeFSLJtIkfxzVbVl1T1W6r6v3MfX0qsbPc9D4jIB8Zc9nkRmdBBGyKyWES+kvt8qYh8Kff5MhE5ayLLsjwPqmmS4R1keF8o631dXV1pJxTkfdEl2TaR1113i8jzIvJ1EXlXYkV77nrgnDGXnZO7fI+p6q2qemUcQV2G50Gt3GF7noz3hbHe19zcnHZCQd4XXZJtExmgmoF/Bg4FnhaRP4jIZ0Vkv2TSdusm4Izhd1gXkZnAO4C/F5E/ichzInLZ8I1FZJ2IXCYiq0RkdW5eFyJyvoj8qNAPEpElIvKEiDwrItdInuMqqwzPg5ozxfZOdO8LY71v8+bNaScU5H3RJdk2kX1QA6r6O1U9F2gCfgCcBWxIKm43PS3A48BpuYvOAW4EvqaqxwDvA04UkfeN+rZtqno08BPgSxP4cT9S1bmqejhQCZwx3o0GDe+FsvwuF+B9oaz3lZWVpZ1QkPdFl2TbhLcLiEgFQ0/QZwPHACvijpqA4c18t+Q+/iPwMRH5FEP3bTowC3gmd/vluY8rgf85gZ9zkoh8GZgM1APPAbeNvVFPRxsbfvxVegaHzq47+8TT6J+9mDlTsmzZWUL/IOxfOciz7RkOrh4gI8rq9gyz67Ij70Y9vWKQp9oyHFGbJavC2s5SDq/NsrGnhLKSoX0NK3dkmDMlS/eAsL67lPfWZFnXXUpNRplW/ub1HVlhU08Jh9QM0NonHFnXz5QyHbl+R7/Q0lfCgVUDvNRRyvTKQWoyb17f0ldCR1aYOXmANR0ZZkweYHLpm9fHeZ/Wd5dyYkPfhO7TK12lTCsf3Cv3aVNvCSc29CX+OEW9T/2DcGJDX2yP08aNG6mvr2fr1q3U1tYyMDBAV1cXzc3NbN68mbKyMurq6ti2bRt1dXX09fXR09Mzcn15eTk1NTW0tLQwdepUstks69evH7m+oqKCyspKtm/fzrRp0+jo6KCvr2/k+srKSsrLy2lra6OhoYG2tjb6+/tHrq+qqqK0tJT29nYaGxtpbW1FVWlsbGTLli0j77Dd2dlJU1MTW7duRUTy3qfu7m5ef/31Cd2nnp4eent798p96u7upqWlZUL3KcrjFOU+lZSUsH79+qDHKR/Z07MhisjpwN8Di4HngRuAG1Q1tXVPEakG/szQWtT1uY93A3NVdbuILAMeUNVlIrIOOEZVt4nIMcB3VXWhiJyfu/wzIrIU6FTV7+a+9/bcv/W522zI3QZVXTq2Z/kdd+sVz9mcMHliQx8PbitPOyMv7wsTd99dnzwqtmUBrF+/nhkzZsS6zDh5X3ShbatWrVq5aNGiY8a7biL7oL4LvAgcparHqer30xycAFS1E3gA+DlDA1Qt0AW0iUgT8MEYfkxF7uO23ICY98i+nYN2N7Os67Y5cA7zvjDW++rq6tJOKMj7okuybY838anqrMQqwlzP0Ka7c1T1BRF5kqFNcH8GHglduKruEJGfAquBdcAT+W5band8oiZj9wAO8L5Q1vv6+vrSTijI+6JLsm2ip9uYzdDbGzUwal6qqi6JN2vPqepvx7Scn+d2M0d9/idgYe7zZcCy3OdLx1uOqn4d+PruWjKG50FNK7c9T8b7wljv6+npSTuhIO+LLsm2iUzU/RRDayQnA/8CHAF8ETgombTi4/OgovO+MNb7LM/jAe8LYWUe1JeB01T1b4Ge3MezgP5EyoqQz4OKzvvCWO+zPI8HvC+EiXlQwH6qOnxI+aCIlKjq74EPJdBVlAYMz4PqyNptA+8LZb2vvNzuEZDgfSGSbJvIdoGNIjJTVdcBLwEfFpFtgN29d3tZv+HdAJuMv9u194Wx3ldTU5N2QkHeF12SbRP5q/4OMHx6928C1wH3AZfl/Y63mQrD54M6pMbuuyGD94Wy3jc8ydQq74suybaJHGa+bNTnvxeRqUB5bi6Sw/Y8qFe6bM+T8b4w1vumTp2adkJB3hddkm0F16BEpCTfPyALdOc+d9ieB2X9MGTvC2O9z/Jh0uB9IZJs290aVBYotN1Kctfbfvm2l1ieBzWlzG4beF8o6329vb1pJxTkfdEl2ba7AepdDJ3C4vXECvYhPg8qOu8LY73P8jwe8L4Qqc2DUtX1wJ2qun74H/Dvo7/OXebweVAhvC+M9T7L83jA+0KkPQ9q7GrBwgQ69glZtbsGtaPfbht4XyjrfRUVFbu/UYq8L7ok2/ZkgLK7WmDMgOHfVEuf7WNZvC+M9b7Kysq0EwryvuiSbNuTv+qMiJwkIieLyMljv85d5oBJhudBHVhle56M94Wx3rd9+/a0EwryvuiSbNvtCQtzJ/ordCNV1XfHGVWsHn74YT3ssMPSzhhXZ2fnbs9emSbvC+N9YbwvutC2Qics3O2hP6NPU+EKGxy0Oxelo6PD7B84eF8o7wvjfdEl2WZ7w3WRsTxAWT7hGXhfKO8L433RJdnmA1SMysrK0k7Iy/I8CvC+UN4Xxvuis3I+KLcb/f12T41leR4FeF8o7wvjfdGlPQ/K7aGSEru/TsuHqYL3hfK+MN4XXdqHmbs9JGJ3sqTlE56B94XyvjDeF12SbT5AxWhgwO5clLa2trQTCvK+MN4XxvuiS7LNB6gYZTJ237CzoaEh7YSCvC+M94XxvuiSbPMBKka+BhWd94XxvjDeF52vQRWJ3b0rR5osH2EI3hfK+8J4X3RJtvkAFSOfBxWd94XxvjDeF53PgyoSll/lWJ5HAd4XyvvCeF90Pg+qSJSW2j3zfVVVVdoJBXlfGO8L433RJdnmA9TbhOXBE7wvlPeF8b7okmzzASpGlo/ia29vTzuhIO8L431hvC+6JNt8gIqR5YMkGhsb004oyPvCeF8Y74suyTYfoGKUzWbTTsirtbU17YSCvC+M94XxvuiSbPMB6m3C8hwt8L5Q3hfG+6JLss0HqBhZfqsjy5sIwPtCeV8Y74vON/EVCcvzoLZs2ZJ2QkHeF8b7wnhfdEm2+QAVI8uHglZXV6edUJD3hfG+MN4XXZJtPkA555wzyQeoGFmeB9XZ2Zl2QkHeF8b7wnhfdEm2+QAVI8vzoJqamtJOKMj7wnhfGO+LLsk2H6BiZHke1NatW9NOKMj7wnhfGO+LLsk2H6DeJkQk7YSCvC+M94XxvuiSbPMBKkaW50HV19ennVCQ94XxvjDeF12SbT5AxcjyPCjLmwjA+0J5Xxjvi8438RUJy/Ogamtr004oyPvCeF8Y74suyTYfoN4mLB8CD94XyvvCeF90Sbb5ABUjy39EXV1daScU5H1hvC+M90WXZJsPUDGyPA+qubk57YSCvC+M94XxvuiSbPMBKkaWD5LYvHlz2gkFeV8Y7wvjfdEl2eYDVIwsz1WwvHYH3hfK+8J4X3RJtvkAFSPLR/HV1dWlnVCQ94XxvjDeF12SbXZnlhahN9p7+MK1T6adMa4TG/p4cFt52hl5eV8Y7wvjfdH99NR6qqqqElm2r0HFaOeg3U1867rtrt2B94XyvjDeF12Sa1A+QMWo1O74RE1G004oyPvCeF8Y74uur68vsWX7ABWjjNj9I5pWPph2QkHeF8b7wnhfdD09PYkt2weoGHUN2F2FWrnD9u5G7wvjfWG8LzqfB1UkqkrtrkHNmWL3XFXgfaG8L4z3RefzoIrEAHbXoDqydtvA+0J5Xxjvi668PLmjC32AilG/3c3EbOqx/VB7XxjvC+N90dXU1CS2bLv3ughVlNjdxHdIjd03sgXvC+V9YbwvupaWlsSW7QNUjCzPg3qly+48CvC+UN4Xxvuimzp1amLL9gEqRpbnQVk+TBW8L5T3hfG+6Pww8yJheR7UlDK7beB9obwvjPdF19vbm9iyfYCKkc+Dis77wnhfGO+LzudBFQmfBxWd94XxvjDeF90+Ow9KRJpF5AYReUVEnheRO0TkUyJye4I/c+Huli8is0Xk9IkuO6t216B29NttA+8L5X1hvC+6ioqKxJad2gAlQ2f3+y3wgKoeqKqzgEuBprSaRpkNTHiAGrC7AkVLn+2VZe8L431hvC+6ysrKxJad5r0+CehX1auHL1DVp4AVQLWI3CQiL4jIr3KDGSKySESeFJHVIvJzEZmUu3yuiDwqIk+LyOMiUiMiFSLyi9xtnxSRk8YGiMhf5b7vydzH94hIOfBN4GwReUpEzhaRqtzPeyJ32w+Pd4cmGZ4HdWCV3XkU4H2hvC+M90W3ffv2xJad5p63w4GVea47CjgMeB14BJgvIn8ClgGLVPUlEfm/wKdF5CrgRuBsVX1CRGqBHuBzAKp6hIgcCtwlIoeM+TkvACeoalZETgEuV9WPiMgS4BhV/QyAiFwO3Keq/ygiU4DHReQeVe0avbBew/OgXuqwO48CvC+U94XxvuimTZuW2LKtHhryuKpuBBCRp4CZQAfwqqq+lLvNL4GLgXuBTar6BICqtue+73jgh7nLXhCR9cDYAaoO+KWIHAwoUJan56+BxSLypdzXFcD/ANaMvpF2t7Hhx1+lZxAmlcDsE0+jf/Zi5kzJsmVnCf2DsH/lIM+2Zzi4eoCMKKvbM8yuy7Kpd2hldnrFIE+1ZTiiNktWhbWdpRxem2VjTwllJdA0aZCVOzLMmZKle0BY313Ke2uyrOsupSajTCt/8/qOrLCpp4RDaoZ+1n59g0wp05Hrd/QLLX0lHFg1wEsdpUyvHKQm8+b1LX0ldGSFmZMHWNORYcbkASaXvnl9nPepb3BotvxE7tMrXaVMK98790kZ6kv6cYp6n6ZXDHDIYPKPU9T7dPSUftr6B0z+7a3ckeHExj5e6cqY/Ntb3T7Ut2pHmcm/vTfeeIOWlhYaGhpoa2ujv7+f5uZmNm/eTFVVFaWlpbS3t9PY2EhrayuqSmNjI1u2bKG6urrgQCCq6WyWEpFFwP9R1RPGXL4Q+JKqnpH7+kfAn4CngP8Yvn3u+y8GlgJXqerxY5Zzc+729+W+XpG7ff3w8kVkGbBKVf9DRGYytD9spoicz65rUCuBv1fVFwvdp+V33K1XPGfzlY7lU0aD94XyvjDeF91PT61nxowZkb9/1apVKxctWnTMeNeluQ/qPmCSiFw0fIGIzAVOzHP7F4CZInJQ7uuPAw/mLn9H7nvJ7X/KAA8B/5C77BCG1njGDjB1wF9yn58/6vIOYPQ7IN4JfHbUvrCjxgv0eVDReV8Y7wvjfdHtk/OgdGjV7W+BU3OHmT/H0NrQ63lu3wtcAPxaRFYDg8DVqtoHnA38UESeBu5maBPcVUBp7rY3Auer6s4xi/0OcIWIPAKMXvW5H5g1fJAE8K8Mbf57RkSezX39Fj4PKjrvC+N9YbwvuiTnQaU6LKvq68DHxrnqp6Nu85lRn9/L0AEUY5fzBHDcOMs5f5zbPgA8kPv8D+y6X+obuctbgbljvvWfxr0To1ieB2X5MFXwvlDeF8b7ottXDzPf51ieB2X5hGfgfaG8L4z3RecnLCwSludBzZxsdx4FeF8o7wvjfdG1tbUltmwfoGLUY3ge1JoOuztZwftCeV8Y74uuoaEhsWX7ABWjSYZ/mzMMvwID7wvlfWG8LzpfgyoSJdjdxDfZ8BGG4H2hvC+M90XX39+f2LJ9gIqRz4OKzvvCeF8Y74tun5wHtS/yeVDReV8Y7wvjfdHts+eD2tf0G54HtWWn7Yfa+8J4Xxjvi66qqiqxZdu910XI7voT9A+mXVCY94XxvjDeF11paXLvP+oDVIzKxe4QtX+l4b9wvC+U94Xxvuja29sTW7YPUDGyPA/q2Xa7O1nB+0J5Xxjvi66xsTGxZfsAFSPL86AOrrY7jwK8L5T3hfG+6FpbWxNbtuGn1OIjhvdCZQxvfgTvC+V9YbwvuiTPKZjaCQv3RY888ojOmjUr7Yxx9fb2UlFRkXZGXt4XxvvCeF90oW1WT1i4z0lyRnWoLVu2pJ1QkPeF8b4w3hddkm0+QMUoycMtQ1VXV6edUJD3hfG+MN4XXZJtPkA555wzyQeoGA0M2D3SprOzM+2EgrwvjPeF8b7okmzzASpGZWVlaSfk1dTUlHZCQd4XxvvCeF90Sbb5ABWjbNbuGzpu3bo17YSCvC+M94XxvuiSbPMB6m1CxO67XID3hfK+MN4XXZJtPkDFKJOx+3Yk9fX1aScU5H1hvC+M90WXZJsPUDGyPA/K8iYC8L5Q3hfG+6LzTXxFwvI8qNra2rQTCvK+MN4XxvuiS7LNB6i3CcuHwIP3hfK+MN4XXZJtPkDFyPIfUVdXV9oJBXlfGO8L433RJdnmA1SMLM+Dam5uTjuhIO8L431hvC+6JNt8gIqR5YMkNm/enHZCQd4XxvvCeF90Sbb5ABWjHTt2pJ2Q180335x2QkHeF8b7wnhfdEm2+QAVI8sD1PLly9NOKMj7wnhfGO+LLsk2H6BiZPnkj5bfhgm8L5T3hfG+6JJs8zPqxuiOO+7omDRp0otpd4yntbW1ob6+flvaHfl4XxjvC+N90cXQNmPRokWN413hA5RzzjmTfBOfc845k3yAcs45Z5IPUDEQkdNE5EUReVlEvpJ2z2gicoCI3C8ia0TkORH5XNpN4xGRUhF5UkRuT7tlLBGZIiI3icgLud/j+9NuGiYiX8g9rs+KyPUiUmGg6eci8oaIPDvqsnoRuVtE1uY+TjXU9m+5x/YZEfmtiExJoy1f36jrviQiKiINabTlGsbtE5HP5p4DnxOR78T183yACiQipcCPgQ8Cs4C/E5FZ6VbtIgt8UVXfCxwHXGysb9jngDVpR+TxA+D/qeqhwJEY6RSRdwKXAMeo6uFAKXBOulUALANOG3PZV4B7VfVg4N7c12lYxlvb7gYOV9X3AS8BX93bUaMs4619iMgBwKnAa3s7aIxljOkTkZOADwPvU9XDgO/G9cN8gAr3V8DLqvpnVe0DbmDowTJBVTep6qrc5x0MPbm+M92qXYnI/sDfANem3TKWiNQCJwA/A1DVPlXdkWrUrjJApYhkgMnA6yn3oKoPAa1jLv4w8Mvc578EztybTcPGa1PVu1R1+FjpPwL77/WwN1vG+90B/DvwZSDVo9ry9H0auFJVd+Zu80ZcP88HqHDvBDaM+nojxgaAYSIyEzgKeCzllLG+z9B/vsGUO8bzbmAr8IvcJshrRaQq7SgAVf0LQ69WXwM2AW2qele6VXk1qeomGHrRBOyXck8+/wj8Pu2I0URkMfAXVX067ZY8DgEWiMhjIvKgiMyNa8E+QIUb73zH5o7dF5Fq4DfA51W1Pe2eYSJyBvCGqq5MuyWPDHA08BNVPQroIr3NU7vI7cf5MPAu4B1AlYicm25V8RKRrzG0SfxXabcME5HJwNeAJWm3FJABpjK0C+Gfgf+WmM4D7wNUuI3AAaO+3h8Dm1lGE5EyhganX6mqtfdMmQ8sFpF1DG0ePVlErks3aRcbgY2qOrzWeRNDA5YFpwCvqupWVe0HlgPzUm7KZ4uITAfIfYxtM1AcROQ84AzgH9TW5NADGXoB8nTu/8j+wCoRsfT25huB5TrkcYa2hMRyIIcPUOGeAA4WkXeJSDlDO6lvTblpRO6VzM+ANar6vbR7xlLVr6rq/qo6k6Hf3X2qamYtQFU3AxtE5D25ixYBz6eYNNprwHEiMjn3OC/CyAEc47gVOC/3+XnALSm27EJETgP+BVisqt1p94ymqqtVdT9VnZn7P7IRODr3d2nFzcDJACJyCFAOxPKuFz5ABcrtXP0McCdDTw7/rarPpVu1i/nAxxlaM3kq9+/0tKOKzGeBX4nIM8Bs4PJ0c4bk1upuAlYBqxn6/3xNqlGAiFwP/AF4j4hsFJELgSuBU0VkLUNHo11pqO1HQA1wd+7/x9VptBXoMyNP38+Bd+cOPb8BOC+utVB/qyPnnHMm+RqUc845k3yAcs45Z5IPUM4550zyAco555xJPkA555wzyQco55xzJvkA5ZxzziQfoJxzzpn0/wGHSs4nJV7GGQAAAABJRU5ErkJggg==\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"icecream.sort_values('Number of Cartons', ascending=False).plot.barh('Flavor')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This bar chart contains exactly the same information as the previous ones, but it is a little easier to read. While this is not a huge gain in reading a chart with just three bars, it can be quite significant when the number of categories is large."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Grouping Categorical Data\n",
"To construct the df `icecream`, someone had to look at all 30 cartons of ice-cream and count the number of each flavor. But if our data does not already include frequencies, we have to compute the frequencies before we can draw a bar chart. Here is an example where this is necessary.\n",
"\n",
"The df `top` consists of U.S.A.'s top grossing movies of all time. The first column contains the title of the movie; *Star Wars: The Force Awakens* has the top rank, with a box office gross amount of more than 900 million dollars in the United States. The second column contains the name of the studio that produced the movie. The third contains the domestic box office gross in dollars, and the fourth contains the gross amount that would have been earned from ticket sales at 2016 prices. The fifth contains the release year of the movie. \n",
"\n",
"There are 200 movies on the list. Here are the top ten according to unadjusted gross receipts."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
Title
\n",
"
Studio
\n",
"
Gross
\n",
"
Gross (Adjusted)
\n",
"
Year
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
Star Wars: The Force Awakens
\n",
"
Buena Vista (Disney)
\n",
"
906723418
\n",
"
906723400
\n",
"
2015
\n",
"
\n",
"
\n",
"
1
\n",
"
Avatar
\n",
"
Fox
\n",
"
760507625
\n",
"
846120800
\n",
"
2009
\n",
"
\n",
"
\n",
"
2
\n",
"
Titanic
\n",
"
Paramount
\n",
"
658672302
\n",
"
1178627900
\n",
"
1997
\n",
"
\n",
"
\n",
"
3
\n",
"
Jurassic World
\n",
"
Universal
\n",
"
652270625
\n",
"
687728000
\n",
"
2015
\n",
"
\n",
"
\n",
"
4
\n",
"
Marvel's The Avengers
\n",
"
Buena Vista (Disney)
\n",
"
623357910
\n",
"
668866600
\n",
"
2012
\n",
"
\n",
"
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
\n",
"
\n",
"
195
\n",
"
The Caine Mutiny
\n",
"
Columbia
\n",
"
21750000
\n",
"
386173500
\n",
"
1954
\n",
"
\n",
"
\n",
"
196
\n",
"
The Bells of St. Mary's
\n",
"
RKO
\n",
"
21333333
\n",
"
545882400
\n",
"
1945
\n",
"
\n",
"
\n",
"
197
\n",
"
Duel in the Sun
\n",
"
Selz.
\n",
"
20408163
\n",
"
443877500
\n",
"
1946
\n",
"
\n",
"
\n",
"
198
\n",
"
Sergeant York
\n",
"
Warner Bros.
\n",
"
16361885
\n",
"
418671800
\n",
"
1941
\n",
"
\n",
"
\n",
"
199
\n",
"
The Four Horsemen of the Apocalypse
\n",
"
MPC
\n",
"
9183673
\n",
"
399489800
\n",
"
1921
\n",
"
\n",
" \n",
"
\n",
"
200 rows × 5 columns
\n",
"
"
],
"text/plain": [
" Title Studio Gross \\\n",
"0 Star Wars: The Force Awakens Buena Vista (Disney) 906723418 \n",
"1 Avatar Fox 760507625 \n",
"2 Titanic Paramount 658672302 \n",
"3 Jurassic World Universal 652270625 \n",
"4 Marvel's The Avengers Buena Vista (Disney) 623357910 \n",
".. ... ... ... \n",
"195 The Caine Mutiny Columbia 21750000 \n",
"196 The Bells of St. Mary's RKO 21333333 \n",
"197 Duel in the Sun Selz. 20408163 \n",
"198 Sergeant York Warner Bros. 16361885 \n",
"199 The Four Horsemen of the Apocalypse MPC 9183673 \n",
"\n",
" Gross (Adjusted) Year \n",
"0 906723400 2015 \n",
"1 846120800 2009 \n",
"2 1178627900 1997 \n",
"3 687728000 2015 \n",
"4 668866600 2012 \n",
".. ... ... \n",
"195 386173500 1954 \n",
"196 545882400 1945 \n",
"197 443877500 1946 \n",
"198 418671800 1941 \n",
"199 399489800 1921 \n",
"\n",
"[200 rows x 5 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"top = pd.read_csv(path_data + 'top_movies.csv')\n",
"top"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Disney subsidiary Buena Vista shows up frequently in the top ten, as do Fox and Warner Brothers. Which studios will appear most frequently if we look among all 200 rows?\n",
"\n",
"To figure this out, first notice that all we need is a table with the movies and the studios; the other information is unnecessary. Again, notice that we use two sqaure brackets as we wish to use more than one column."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"movies_and_studios = top[['Title', 'Studio']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The df methods `groupby()` and `count()` allows us to count how frequently each studio appears in the table, by calling each studio a category and assigning each row to one category. The `groupby` method takes as its argument the label of the column that contains the categories, the `count()` returns a table of counts of rows in each category. Unless we state which column we wish to `count()` the first column in the df will be used, notice how the name of the grouping column or category is shown below the name of the column being used for the count. There a number of ways by which we could 'remove' the name of the count column - can you think of any?"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
Title
\n",
"
\n",
"
\n",
"
Studio
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
AVCO
\n",
"
1
\n",
"
\n",
"
\n",
"
Buena Vista (Disney)
\n",
"
29
\n",
"
\n",
"
\n",
"
Columbia
\n",
"
10
\n",
"
\n",
"
\n",
"
Disney
\n",
"
11
\n",
"
\n",
"
\n",
"
Dreamworks
\n",
"
3
\n",
"
\n",
"
\n",
"
Fox
\n",
"
26
\n",
"
\n",
"
\n",
"
IFC
\n",
"
1
\n",
"
\n",
"
\n",
"
Lionsgate
\n",
"
3
\n",
"
\n",
"
\n",
"
MGM
\n",
"
7
\n",
"
\n",
"
\n",
"
MPC
\n",
"
1
\n",
"
\n",
"
\n",
"
NM
\n",
"
1
\n",
"
\n",
"
\n",
"
New Line
\n",
"
5
\n",
"
\n",
"
\n",
"
Orion
\n",
"
1
\n",
"
\n",
"
\n",
"
Paramount
\n",
"
25
\n",
"
\n",
"
\n",
"
Paramount/Dreamworks
\n",
"
4
\n",
"
\n",
"
\n",
"
RKO
\n",
"
3
\n",
"
\n",
"
\n",
"
Selz.
\n",
"
1
\n",
"
\n",
"
\n",
"
Sony
\n",
"
6
\n",
"
\n",
"
\n",
"
Sum.
\n",
"
2
\n",
"
\n",
"
\n",
"
TriS
\n",
"
2
\n",
"
\n",
"
\n",
"
UA
\n",
"
6
\n",
"
\n",
"
\n",
"
Universal
\n",
"
22
\n",
"
\n",
"
\n",
"
Warner Bros.
\n",
"
29
\n",
"
\n",
"
\n",
"
Warner Bros. (New Line)
\n",
"
1
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Title\n",
"Studio \n",
"AVCO 1\n",
"Buena Vista (Disney) 29\n",
"Columbia 10\n",
"Disney 11\n",
"Dreamworks 3\n",
"Fox 26\n",
"IFC 1\n",
"Lionsgate 3\n",
"MGM 7\n",
"MPC 1\n",
"NM 1\n",
"New Line 5\n",
"Orion 1\n",
"Paramount 25\n",
"Paramount/Dreamworks 4\n",
"RKO 3\n",
"Selz. 1\n",
"Sony 6\n",
"Sum. 2\n",
"TriS 2\n",
"UA 6\n",
"Universal 22\n",
"Warner Bros. 29\n",
"Warner Bros. (New Line) 1"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"movies_and_studios.groupby(['Studio']).count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Thus `groupby` creates a distribution table that shows how the movies are distributed among the categories (studios). \n",
"\n",
"We can now use this table, along with the graphing skills that we acquired above, to draw a bar chart that shows which studios are most frequent among the 200 highest grossing movies."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"