diff --git a/2 - Pandas Series exercises.ipynb b/2 - Pandas Series exercises.ipynb
index 4213ac6..7947e7d 100644
--- a/2 - Pandas Series exercises.ipynb
+++ b/2 - Pandas Series exercises.ipynb
@@ -1,947 +1,1409 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "\n",
- "# Pandas Series exercises\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.25.3\n"
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "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": "3.7.4"
+ },
+ "colab": {
+ "name": "2 - Pandas Series exercises.ipynb",
+ "provenance": []
}
- ],
- "source": [
- "# Import the numpy package under the name np\n",
- "import numpy as np\n",
- "\n",
- "# Import the pandas package under the name pd\n",
- "import pandas as pd\n",
- "\n",
- "# Print the pandas version and the configuration\n",
- "print(pd.__version__)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Series creation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create an empty pandas Series"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "pd.Series()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X python list convert it to an Y pandas Series"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = ['A','B','C']\n",
- "print(X, type(X))\n",
- "\n",
- "Y = pd.Series(X)\n",
- "print(Y, type(Y)) # different type"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, name it 'My letters'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series(['A','B','C'])\n",
- "\n",
- "X.name = 'My letters'\n",
- "X"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, show its values\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series(['A','B','C'])\n",
- "\n",
- "X.values"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Series indexation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Assign index names to the given X pandas Series\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series(['A','B','C'])\n",
- "index_names = ['first', 'second', 'third']\n",
- "\n",
- "X.index = index_names\n",
- "X"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, show its first element\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series(['A','B','C'], index=['first', 'second', 'third'])\n",
- "\n",
- "#X[0] # by position\n",
- "#X.iloc[0] # by position\n",
- "X['first'] # by index"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, show its last element\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series(['A','B','C'], index=['first', 'second', 'third'])\n",
- "\n",
- "#X[-1] # by position\n",
- "#X.iloc[-1] # by position\n",
- "X['third'] # by index"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, show all middle elements\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series(['A','B','C','D','E'],\n",
- " index=['first','second','third','forth','fifth'])\n",
- "\n",
- "#X[['second', 'third', 'forth']]\n",
- "#X.iloc[1:-1] # by position\n",
- "X[1:-1] # by position"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, show the elements in reverse position\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series(['A','B','C','D','E'],\n",
- " index=['first','second','third','forth','fifth'])\n",
- "\n",
- "#X.iloc[::-1]\n",
- "X[::-1]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, show the first and last elements\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series(['A','B','C','D','E'],\n",
- " index=['first','second','third','forth','fifth'])\n",
- "\n",
- "#X[['first', 'fifth']]\n",
- "#X.iloc[[0, -1]]\n",
- "X[[0, -1]]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Series manipulation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Convert the given integer pandas Series to float\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series([1,2,3,4,5],\n",
- " index=['first','second','third','forth','fifth'])\n",
- "\n",
- "pd.Series(X, dtype=np.float)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Reverse the given pandas Series (first element becomes last)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series([1,2,3,4,5],\n",
- " index=['first','second','third','forth','fifth'])\n",
- "\n",
- "X[::-1]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Order (sort) the given pandas Series\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series([4,2,5,1,3],\n",
- " index=['forth','second','fifth','first','third'])\n",
- "\n",
- "X = X.sort_values()\n",
- "X"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, set the fifth element equal to 10\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series([1,2,3,4,5],\n",
- " index=['A','B','C','D','E'])\n",
- "\n",
- "X[4] = 10\n",
- "X"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, change all the middle elements to 0\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution",
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "X = pd.Series([1,2,3,4,5],\n",
- " index=['A','B','C','D','E'])\n",
- "\n",
- "X[1:-1] = 0\n",
- "X"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, add 5 to every element\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series([1,2,3,4,5])\n",
- "\n",
- "X + 5"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Series boolean arrays (also called masks)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Given the X pandas Series, make a mask showing negative elements\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\n",
- "\n",
- "mask = X <= 0\n",
- "mask"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, get the negative elements\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\n",
- "\n",
- "mask = X <= 0\n",
- "X[mask]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, get numbers higher than 5\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\n",
- "\n",
- "mask = X > 5\n",
- "X[mask]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, get numbers higher than the elements mean"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\n",
- "\n",
- "mask = X > X.mean()\n",
- "X[mask]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, get numbers equal to 2 or 10\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution",
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\n",
- "\n",
- "mask = (X == 2) | (X == 10)\n",
- "X[mask]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Logic functions"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Given the X pandas Series, return True if none of its elements is zero"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\n",
- "\n",
- "X.all()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, return True if any of its elements is zero\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\n",
- "\n",
- "X.any()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Summary statistics"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Given the X pandas Series, show the sum of its elements\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series([3,5,6,7,2,3,4,9,4])\n",
- "\n",
- "#np.sum(X)\n",
- "X.sum()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, show the mean value of its elements"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series([1,2,0,4,5,6,0,0,9,10])\n",
- "\n",
- "#np.mean(X)\n",
- "X.mean()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the X pandas Series, show the max value of its elements"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "X = pd.Series([1,2,0,4,5,6,0,0,9,10])\n",
- "\n",
- "#np.max(X)\n",
- "X.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- }
- ],
- "metadata": {
- "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": "3.7.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ybDWVcMjbMp-"
+ },
+ "source": [
+ "\n",
+ "
\n",
+ "\n",
+ "# Pandas Series exercises\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Tb2rsdVpbMqH",
+ "outputId": "6ab94c1f-4e97-46e9-db1a-e10a2bb3dbcc"
+ },
+ "source": [
+ "# Import the numpy package under the name np\n",
+ "import numpy as np\n",
+ "\n",
+ "# Import the pandas package under the name pd\n",
+ "import pandas as pd\n",
+ "\n",
+ "# Print the pandas version and the configuration\n",
+ "print(pd.__version__)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "0.25.3\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ml8toXCFbMqK"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "## Series creation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VmFbr-xEbeXI"
+ },
+ "source": [
+ "X = ['A','B','C']\r\n",
+ "\r\n",
+ "# use 'x' to solve problems\r\n",
+ "x = X"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Mnp8dk16bMqK"
+ },
+ "source": [
+ "\r\n",
+ "\r\n",
+ "### Create an empty pandas Series"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DhXlWbsgbMqL"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BdvIUcaXbODi"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "bMkmWO9qbMqM"
+ },
+ "source": [
+ "pd.Series()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6c_sZxmHbMqN"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X python list convert it to an Y pandas Series"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "arwXpucJbMqO"
+ },
+ "source": [
+ "print(X, type(X))\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wjbtaFKibUL_"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "36yq8IyybMqO"
+ },
+ "source": [
+ "print(X, type(X))\n",
+ "\n",
+ "Y = pd.Series(X)\n",
+ "print(Y, type(Y)) # different type"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gq-niNjHbMqQ"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, name it 'My letters'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "m0DwoDRKbMqR"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BEByVL9Ob6RY"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "O57B0DwbbMqS"
+ },
+ "source": [
+ "X.name = 'My letters'\n",
+ "X"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ru93Ox4mbMqS"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, show its values\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EeKlQ0xtbMqT"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "R5CKr2V0cD0V"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "jw9jjOkmbMqT"
+ },
+ "source": [
+ "X.values"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EQScgSBGbMqT"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "## Series indexation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "elLEOculbMqT"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Assign index names to the given X pandas Series\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "C3lUoLxvbMqU"
+ },
+ "source": [
+ "x = pd.Series(['A','B','C'])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6oGaFTl2cOs_"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "5xyTYknCbMqU"
+ },
+ "source": [
+ "X = pd.Series(['A','B','C'])\n",
+ "index_names = ['first', 'second', 'third']\n",
+ "\n",
+ "X.index = index_names\n",
+ "X"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8dZFx4Z_bMqV"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, show its first element\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "gFfs9OVcbMqV"
+ },
+ "source": [
+ "x = pd.Series(['A','B','C'], index=['first', 'second', 'third'])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8DGtME8YdVpH"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "i_Lu5xR0bMqV"
+ },
+ "source": [
+ "X = pd.Series(['A','B','C'], index=['first', 'second', 'third'])\n",
+ "\n",
+ "#X[0] # by position\n",
+ "#X.iloc[0] # by position\n",
+ "X['first'] # by index"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RytSngYSbMqV"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, show its last element\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1teZ4A5JbMqW"
+ },
+ "source": [
+ "x = pd.Series(['A','B','C'], index=['first', 'second', 'third'])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "D1XRYspTdZTl"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "_43opeJPbMqW"
+ },
+ "source": [
+ "X = pd.Series(['A','B','C'], index=['first', 'second', 'third'])\n",
+ "\n",
+ "#X[-1] # by position\n",
+ "#X.iloc[-1] # by position\n",
+ "X['third'] # by index"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "m-JWkFAubMqW"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, show all middle elements\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "GjPJekJkbMqW"
+ },
+ "source": [
+ "x = pd.Series(['A','B','C','D','E'],\n",
+ " index=['first','second','third','forth','fifth'])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fOQFJHmMdZ3-"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "WCpl4DD3bMqX"
+ },
+ "source": [
+ "X = pd.Series(['A','B','C','D','E'],\n",
+ " index=['first','second','third','forth','fifth'])\n",
+ "\n",
+ "#X[['second', 'third', 'forth']]\n",
+ "#X.iloc[1:-1] # by position\n",
+ "X[1:-1] # by position"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mJy-1hXEbMqX"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, show the elements in reverse position\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "x_cKQKyebMqX"
+ },
+ "source": [
+ "x = pd.Series(['A','B','C','D','E'],\n",
+ " index=['first','second','third','forth','fifth'])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zSqp7hqHdafW"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "lw-u_eS-bMqX"
+ },
+ "source": [
+ "X = pd.Series(['A','B','C','D','E'],\n",
+ " index=['first','second','third','forth','fifth'])\n",
+ "\n",
+ "#X.iloc[::-1]\n",
+ "X[::-1]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mnhnni46bMqY"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, show the first and last elements\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "y_O1oQXJbMqY"
+ },
+ "source": [
+ "x = pd.Series(['A','B','C','D','E'],\n",
+ " index=['first','second','third','forth','fifth'])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jaBPE0kQdbKC"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "w7RqwiGSbMqY"
+ },
+ "source": [
+ "X = pd.Series(['A','B','C','D','E'],\n",
+ " index=['first','second','third','forth','fifth'])\n",
+ "\n",
+ "#X[['first', 'fifth']]\n",
+ "#X.iloc[[0, -1]]\n",
+ "X[[0, -1]]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "D401INVMbMqY"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "## Series manipulation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GdTJdnaPbMqY"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Convert the given integer pandas Series to float\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4YJV0Wb6bMqZ"
+ },
+ "source": [
+ "x = pd.Series([1,2,3,4,5],\n",
+ " index=['first','second','third','forth','fifth'])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yQDnY3JOeIem"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "RMOpQWZrbMqZ"
+ },
+ "source": [
+ "X = pd.Series([1,2,3,4,5],\n",
+ " index=['first','second','third','forth','fifth'])\n",
+ "\n",
+ "pd.Series(X, dtype=np.float)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SMkdBcCHbMqZ"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Reverse the given pandas Series (first element becomes last)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kRig0FuhbMqZ"
+ },
+ "source": [
+ "x = pd.Series([1,2,3,4,5],\n",
+ " index=['first','second','third','forth','fifth'])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rPT148wheMSI"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "l4sqpLlFbMqZ"
+ },
+ "source": [
+ "X = pd.Series([1,2,3,4,5],\n",
+ " index=['first','second','third','forth','fifth'])\n",
+ "\n",
+ "X[::-1]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bxHFoFW3bMqa"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Order (sort) the given pandas Series\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Vux1YcBbbMqa"
+ },
+ "source": [
+ "x = pd.Series([4,2,5,1,3],\n",
+ " index=['forth','second','fifth','first','third'])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CchPBdy6eNLO"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "EJUe--SQbMqa"
+ },
+ "source": [
+ "X = pd.Series([4,2,5,1,3],\n",
+ " index=['forth','second','fifth','first','third'])\n",
+ "\n",
+ "X = X.sort_values()\n",
+ "X"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TCWeGylXbMqa"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, set the fifth element equal to 10\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ni3lwmW0bMqb"
+ },
+ "source": [
+ "x = pd.Series([1,2,3,4,5],\n",
+ " index=['A','B','C','D','E'])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sKlH-pBheN99"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "NEVGeUaAbMqb"
+ },
+ "source": [
+ "X = pd.Series([1,2,3,4,5],\n",
+ " index=['A','B','C','D','E'])\n",
+ "\n",
+ "X[4] = 10\n",
+ "X"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OWKdO32ObMqb"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, change all the middle elements to 0\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HhNhNsLJbMqc"
+ },
+ "source": [
+ "x = pd.Series([1,2,3,4,5],\n",
+ " index=['A','B','C','D','E'])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IrxnrTM3eOib"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "scrolled": false,
+ "id": "xxXXDPiVbMqc"
+ },
+ "source": [
+ "X = pd.Series([1,2,3,4,5],\n",
+ " index=['A','B','C','D','E'])\n",
+ "\n",
+ "X[1:-1] = 0\n",
+ "X"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nZjglA13bMqd"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, add 5 to every element\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2T0U_0sHbMqd"
+ },
+ "source": [
+ "x = pd.Series([1,2,3,4,5])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3GoKzNRSePab"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "gRwmwUnFbMqd"
+ },
+ "source": [
+ "X = pd.Series([1,2,3,4,5])\n",
+ "\n",
+ "X + 5"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HYPkf8HJbMqd"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "## Series boolean arrays (also called masks)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bIYZpiQxe3GS"
+ },
+ "source": [
+ "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\r\n",
+ "\r\n",
+ "# use 'x' to solve problems\r\n",
+ "x = X"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5nUM2Vf5bMqd"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, make a mask showing negative elements\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "q0JRN0FfbMqe"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1qSGhmzGenIm"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "XegTI9x1bMqe"
+ },
+ "source": [
+ "mask = X <= 0\n",
+ "mask"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NkN2soUBbMqe"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, get the negative elements\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dIcxoX5HbMqe"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "93YXd25pevvO"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "eH4gpztWbMqe"
+ },
+ "source": [
+ "mask = X <= 0\n",
+ "X[mask]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5gpLGeeubMqf"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, get numbers higher than 5\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "B05uJE7_bMqh"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "S6-hhTKeevNd"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "zp5VBBBBbMqh"
+ },
+ "source": [
+ "mask = X > 5\n",
+ "X[mask]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-eKYq1zDbMqi"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, get numbers higher than the elements mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "q1H1M5jubMqi"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aKONvl9zeubD"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "aDH5To4NbMqi"
+ },
+ "source": [
+ "mask = X > X.mean()\n",
+ "X[mask]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6cavoIB2bMqi"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, get numbers equal to 2 or 10\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XgrO__PnbMqi"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "E4xY48mJet5t"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "scrolled": true,
+ "id": "sV85mMA6bMqj"
+ },
+ "source": [
+ "mask = (X == 2) | (X == 10)\n",
+ "X[mask]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pCeYr7oqbMqj"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "## Logic functions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vQocn2QkfT91"
+ },
+ "source": [
+ "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\r\n",
+ "\r\n",
+ "# use 'x' to solve problems\r\n",
+ "x = X"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yxG57piJbMqj"
+ },
+ "source": [
+ "\r\n",
+ "\r\n",
+ "### Given the X pandas Series, return True if none of its elements is zero"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "CePlf6lJbMqj"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "b566GNvofbgC"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "GXKWqHiGbMqk"
+ },
+ "source": [
+ "X.all()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ukCqpMnVbMqk"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, return True if any of its elements is zero\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "JX7hp9DsbMqk"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qE_qyWCqffhD"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "F8KfQk7RbMql"
+ },
+ "source": [
+ "X.any()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "q14CVR36bMql"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "## Summary statistics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wRYj5CTdbMqm"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, show the sum of its elements\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vO8LBf-PbMqm"
+ },
+ "source": [
+ "x = pd.Series([3,5,6,7,2,3,4,9,4])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hJIkZCrAgSJB"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "t7Zq1ZlmbMqn"
+ },
+ "source": [
+ "X = pd.Series([3,5,6,7,2,3,4,9,4])\n",
+ "\n",
+ "#np.sum(X)\n",
+ "X.sum()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xEjk7RD6bMqn"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, show the mean value of its elements"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "A0ukQoVzbMqn"
+ },
+ "source": [
+ "X = pd.Series([1,2,0,4,5,6,0,0,9,10])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yZsLR6-fgRis"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "LB4JD1i0bMqo"
+ },
+ "source": [
+ "X = pd.Series([1,2,0,4,5,6,0,0,9,10])\n",
+ "\n",
+ "#np.mean(X)\n",
+ "X.mean()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zr9_DadCbMqo"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the X pandas Series, show the max value of its elements"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9Lx7RuPObMqo"
+ },
+ "source": [
+ "X = pd.Series([1,2,0,4,5,6,0,0,9,10])\n",
+ "\n",
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pdHkpDwigPA2"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "yXsuU4OqbMqo"
+ },
+ "source": [
+ "X = pd.Series([1,2,0,4,5,6,0,0,9,10])\n",
+ "\n",
+ "#np.max(X)\n",
+ "X.max()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fVDWb1sZbMqo"
+ },
+ "source": [
+ ""
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/4 - Pandas DataFrames exercises.ipynb b/4 - Pandas DataFrames exercises.ipynb
index 4227f4d..d11d8ee 100644
--- a/4 - Pandas DataFrames exercises.ipynb
+++ b/4 - Pandas DataFrames exercises.ipynb
@@ -1,882 +1,1243 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "\n",
- "# Pandas DataFrame exercises\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Import the numpy package under the name np\n",
- "import numpy as np\n",
- "\n",
- "# Import the pandas package under the name pd\n",
- "import pandas as pd\n",
- "\n",
- "# Import the matplotlib package under the name plt\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "# Print the pandas version and the configuration\n",
- "print(pd.__version__)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## DataFrame creation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create an empty pandas DataFrame\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "pd.DataFrame(data=[None],\n",
- " index=[None],\n",
- " columns=[None])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "![](\"https://cdn.dribbble.com/users/4678/screenshots/1986600/avengers.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Create a `marvel_df` pandas DataFrame with the given marvel data\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "marvel_data = [\n",
- " ['Spider-Man', 'male', 1962],\n",
- " ['Captain America', 'male', 1941],\n",
- " ['Wolverine', 'male', 1974],\n",
- " ['Iron Man', 'male', 1963],\n",
- " ['Thor', 'male', 1963],\n",
- " ['Thing', 'male', 1961],\n",
- " ['Mister Fantastic', 'male', 1961],\n",
- " ['Hulk', 'male', 1962],\n",
- " ['Beast', 'male', 1963],\n",
- " ['Invisible Woman', 'female', 1961],\n",
- " ['Storm', 'female', 1975],\n",
- " ['Namor', 'male', 1939],\n",
- " ['Hawkeye', 'male', 1964],\n",
- " ['Daredevil', 'male', 1964],\n",
- " ['Doctor Strange', 'male', 1963],\n",
- " ['Hank Pym', 'male', 1962],\n",
- " ['Scarlet Witch', 'female', 1964],\n",
- " ['Wasp', 'female', 1963],\n",
- " ['Black Widow', 'female', 1964],\n",
- " ['Vision', 'male', 1968]\n",
- "]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "marvel_df = pd.DataFrame(data=marvel_data)\n",
- "\n",
- "marvel_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Add column names to the `marvel_df`\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "col_names = ['name', 'sex', 'first_appearance']\n",
- "\n",
- "marvel_df.columns = col_names\n",
- "marvel_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Add index names to the `marvel_df` (use the character name as index)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "marvel_df.index = marvel_df['name']\n",
- "marvel_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Drop the name column as it's now the index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "#marvel_df = marvel_df.drop(columns=['name'])\n",
- "marvel_df = marvel_df.drop(['name'], axis=1)\n",
- "marvel_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Drop 'Namor' and 'Hank Pym' rows\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "marvel_df = marvel_df.drop(['Namor', 'Hank Pym'], axis=0)\n",
- "marvel_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## DataFrame selection, slicing and indexation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Show the first 5 elements on `marvel_df`\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "#marvel_df.loc[['Spider-Man', 'Captain America', 'Wolverine', 'Iron Man', 'Thor'], :] # bad!\n",
- "#marvel_df.loc['Spider-Man': 'Thor', :]\n",
- "#marvel_df.iloc[0:5, :]\n",
- "#marvel_df.iloc[0:5,]\n",
- "marvel_df.iloc[:5,]\n",
- "#marvel_df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Show the last 5 elements on `marvel_df`\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "#marvel_df.loc[['Hank Pym', 'Scarlet Witch', 'Wasp', 'Black Widow', 'Vision'], :] # bad!\n",
- "#marvel_df.loc['Hank Pym':'Vision', :]\n",
- "marvel_df.iloc[-5:,]\n",
- "#marvel_df.tail()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Show just the sex of the first 5 elements on `marvel_df`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "#marvel_df.iloc[:5,]['sex'].to_frame()\n",
- "marvel_df.iloc[:5,].sex.to_frame()\n",
- "#marvel_df.head().sex.to_frame()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Show the first_appearance of all middle elements on `marvel_df` "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "marvel_df.iloc[1:-1,].first_appearance.to_frame()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Show the first and last elements on `marvel_df`\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "#marvel_df.iloc[[0, -1],][['sex', 'first_appearance']]\n",
- "marvel_df.iloc[[0, -1],]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## DataFrame manipulation and operations"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Modify the `first_appearance` of 'Vision' to year 1964"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "marvel_df.loc['Vision', 'first_appearance'] = 1964\n",
- "\n",
- "marvel_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Add a new column to `marvel_df` called 'years_since' with the years since `first_appearance`\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "marvel_df['years_since'] = 2018 - marvel_df['first_appearance']\n",
- "\n",
- "marvel_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## DataFrame boolean arrays (also called masks)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Given the `marvel_df` pandas DataFrame, make a mask showing the female characters\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "mask = marvel_df['sex'] == 'female'\n",
- "\n",
- "mask"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the `marvel_df` pandas DataFrame, get the male characters\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "mask = marvel_df['sex'] == 'male'\n",
- "\n",
- "marvel_df[mask]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the `marvel_df` pandas DataFrame, get the characters with `first_appearance` after 1970\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "mask = marvel_df['first_appearance'] > 1970\n",
- "\n",
- "marvel_df[mask]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the `marvel_df` pandas DataFrame, get the female characters with `first_appearance` after 1970"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution",
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "mask = (marvel_df['sex'] == 'female') & (marvel_df['first_appearance'] > 1970)\n",
- "\n",
- "marvel_df[mask]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## DataFrame summary statistics"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Show basic statistics of `marvel_df`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "marvel_df.describe()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the `marvel_df` pandas DataFrame, show the mean value of `first_appearance`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "\n",
- "#np.mean(marvel_df.first_appearance)\n",
- "marvel_df.first_appearance.mean()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the `marvel_df` pandas DataFrame, show the min value of `first_appearance`\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "#np.min(marvel_df.first_appearance)\n",
- "marvel_df.first_appearance.min()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Given the `marvel_df` pandas DataFrame, get the characters with the min value of `first_appearance`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "mask = marvel_df['first_appearance'] == marvel_df.first_appearance.min()\n",
- "marvel_df[mask]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## DataFrame basic plottings"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Reset index names of `marvel_df`\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "marvel_df = marvel_df.reset_index()\n",
- "\n",
- "marvel_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Plot the values of `first_appearance`\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "#plt.plot(marvel_df.index, marvel_df.first_appearance)\n",
- "marvel_df.first_appearance.plot()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "### Plot a histogram (plot.hist) with values of `first_appearance`\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code goes here\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cell_type": "solution"
- },
- "outputs": [],
- "source": [
- "\n",
- "plt.hist(marvel_df.first_appearance)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n"
- ]
- }
- ],
- "metadata": {
- "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": "3.8.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "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": "3.8.1"
+ },
+ "colab": {
+ "name": "4 - Pandas DataFrames exercises.ipynb",
+ "provenance": []
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DetNK0pQjLX_"
+ },
+ "source": [
+ "\n",
+ "
\n",
+ "\n",
+ "# Pandas DataFrame exercises\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bKcHu6v5jLYI"
+ },
+ "source": [
+ "# Import the numpy package under the name np\n",
+ "import numpy as np\n",
+ "\n",
+ "# Import the pandas package under the name pd\n",
+ "import pandas as pd\n",
+ "\n",
+ "# Import the matplotlib package under the name plt\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "# Print the pandas version and the configuration\n",
+ "print(pd.__version__)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qskaPzfejLYQ"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "## DataFrame creation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bWFGzQHCjLYS"
+ },
+ "source": [
+ "### Create an empty pandas DataFrame\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jk2u9QChjLYS"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FbSbPTLvjVPL"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "_0y_r-YHjLYT"
+ },
+ "source": [
+ "pd.DataFrame(data=[None],\n",
+ " index=[None],\n",
+ " columns=[None])"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wYmJVu3cjLYU"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Create a `marvel_df` pandas DataFrame with the given marvel data\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "t9dXtIC0jLYT"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kd8gTldbjLYU"
+ },
+ "source": [
+ "marvel_data = [\n",
+ " ['Spider-Man', 'male', 1962],\n",
+ " ['Captain America', 'male', 1941],\n",
+ " ['Wolverine', 'male', 1974],\n",
+ " ['Iron Man', 'male', 1963],\n",
+ " ['Thor', 'male', 1963],\n",
+ " ['Thing', 'male', 1961],\n",
+ " ['Mister Fantastic', 'male', 1961],\n",
+ " ['Hulk', 'male', 1962],\n",
+ " ['Beast', 'male', 1963],\n",
+ " ['Invisible Woman', 'female', 1961],\n",
+ " ['Storm', 'female', 1975],\n",
+ " ['Namor', 'male', 1939],\n",
+ " ['Hawkeye', 'male', 1964],\n",
+ " ['Daredevil', 'male', 1964],\n",
+ " ['Doctor Strange', 'male', 1963],\n",
+ " ['Hank Pym', 'male', 1962],\n",
+ " ['Scarlet Witch', 'female', 1964],\n",
+ " ['Wasp', 'female', 1963],\n",
+ " ['Black Widow', 'female', 1964],\n",
+ " ['Vision', 'male', 1968]\n",
+ "]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VXh3cqtHjLYV"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6l2ZKRDijaqs"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "cH4wD_X3jLYW"
+ },
+ "source": [
+ "marvel_df = pd.DataFrame(data=marvel_data)\n",
+ "\n",
+ "marvel_df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iiuxrOppjLYX"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Add column names to the `marvel_df`\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XhCkhHDTjLYX"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GIFutRikjbkP"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "iVIa9fjJjLYY"
+ },
+ "source": [
+ "col_names = ['name', 'sex', 'first_appearance']\n",
+ "\n",
+ "marvel_df.columns = col_names\n",
+ "marvel_df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WY8XWTWzjLYe"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Add index names to the `marvel_df` (use the character name as index)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xrjBjwJbjLYg"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kIAW640PjcNL"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "dYGQaJsJjLYh"
+ },
+ "source": [
+ "marvel_df.index = marvel_df['name']\n",
+ "marvel_df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HPrLJ36yjLYh"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Drop the name column as it's now the index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QqBoWZnAjLYj"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iNcstNC3jird"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "yvYUKNxCjLYj"
+ },
+ "source": [
+ "#marvel_df = marvel_df.drop(columns=['name'])\n",
+ "marvel_df = marvel_df.drop(['name'], axis=1)\n",
+ "marvel_df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LJRIvQrVjLYk"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Drop 'Namor' and 'Hank Pym' rows\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "fcsadtzwjLYk"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rd1AZI7yjggi"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "jsMNzHGQjLYk"
+ },
+ "source": [
+ "marvel_df = marvel_df.drop(['Namor', 'Hank Pym'], axis=0)\n",
+ "marvel_df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uidbW-YCjLYo"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "## DataFrame selection, slicing and indexation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7fDmCKzhjLYp"
+ },
+ "source": [
+ "### Show the first 5 elements on `marvel_df`\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Og71qXfmjLYp"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "k0C6ZQNnj6cA"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "nxqWXkcRjLYt"
+ },
+ "source": [
+ "#marvel_df.loc[['Spider-Man', 'Captain America', 'Wolverine', 'Iron Man', 'Thor'], :] # bad!\n",
+ "#marvel_df.loc['Spider-Man': 'Thor', :]\n",
+ "#marvel_df.iloc[0:5, :]\n",
+ "#marvel_df.iloc[0:5,]\n",
+ "marvel_df.iloc[:5,]\n",
+ "#marvel_df.head()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "26C_Qb_MjLYx"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Show the last 5 elements on `marvel_df`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "80x4Jq95jLYx"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nOP_LBWxj7Cg"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "ekGHxWxwjLYy"
+ },
+ "source": [
+ "#marvel_df.loc[['Hank Pym', 'Scarlet Witch', 'Wasp', 'Black Widow', 'Vision'], :] # bad!\n",
+ "#marvel_df.loc['Hank Pym':'Vision', :]\n",
+ "marvel_df.iloc[-5:,]\n",
+ "#marvel_df.tail()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yBiWXOy1jLYz"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Show just the sex of the first 5 elements on `marvel_df`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "cXbqIbV7jLYz"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "f7g8CyoPj71s"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "uCE4-T-CjLYz"
+ },
+ "source": [
+ "#marvel_df.iloc[:5,]['sex'].to_frame()\n",
+ "marvel_df.iloc[:5,].sex.to_frame()\n",
+ "#marvel_df.head().sex.to_frame()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cPUUIybBjLY0"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Show the first_appearance of all middle elements on `marvel_df` "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xN-9P4ODjLY0"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "R5Sa9WV6j8m7"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "NYvzBUPojLY0"
+ },
+ "source": [
+ "marvel_df.iloc[1:-1,].first_appearance.to_frame()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gAUqL_MhjLY0"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Show the first and last elements on `marvel_df`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4uH4rj6djLY1"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "J1tzMDVMj9QD"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "ZrTZTBXIjLY1"
+ },
+ "source": [
+ "#marvel_df.iloc[[0, -1],][['sex', 'first_appearance']]\n",
+ "marvel_df.iloc[[0, -1],]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FgsOUKIijLY1"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "## DataFrame manipulation and operations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zFFnIckAjLY2"
+ },
+ "source": [
+ "### Modify the `first_appearance` of 'Vision' to year 1964"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PPEHONL0jLY2"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZW7m9G0Mj90R"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "OZWuPfXtjLY2"
+ },
+ "source": [
+ "marvel_df.loc['Vision', 'first_appearance'] = 1964\n",
+ "\n",
+ "marvel_df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bgLi1h50jLY3"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Add a new column to `marvel_df` called 'years_since' with the years since `first_appearance`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0IVAdKqdjLY3"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BsRqXZIWj-l_"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "hsx7RanLjLY3"
+ },
+ "source": [
+ "marvel_df['years_since'] = 2018 - marvel_df['first_appearance']\n",
+ "\n",
+ "marvel_df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pHuEcc9SjLY3"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "## DataFrame boolean arrays (also called masks)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KXPIwRKLjLY4"
+ },
+ "source": [
+ "### Given the `marvel_df` pandas DataFrame, make a mask showing the female characters\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1Im0LUdWjLY4"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2liXysTYkJbp"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "8rhBQ6sGjLY4"
+ },
+ "source": [
+ "mask = marvel_df['sex'] == 'female'\n",
+ "\n",
+ "mask"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gZOssIPtjLY4"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the `marvel_df` pandas DataFrame, get the male characters\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SBJsz-ocjLY4"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Os0C1v7GkKNL"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "qFmzb3xhjLY6"
+ },
+ "source": [
+ "mask = marvel_df['sex'] == 'male'\n",
+ "\n",
+ "marvel_df[mask]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1yZ85OQ1jLZC"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the `marvel_df` pandas DataFrame, get the characters with `first_appearance` after 1970\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3STDMvbLjLZC"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JAzaLfqYkKv8"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "KdlEOkZLjLZC"
+ },
+ "source": [
+ "mask = marvel_df['first_appearance'] > 1970\n",
+ "\n",
+ "marvel_df[mask]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6KYhmXNUjLZD"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the `marvel_df` pandas DataFrame, get the female characters with `first_appearance` after 1970"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "CaAKusZLjLZD"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2la6t8LrkLWq"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "scrolled": true,
+ "id": "CrrasUNljLZD"
+ },
+ "source": [
+ "mask = (marvel_df['sex'] == 'female') & (marvel_df['first_appearance'] > 1970)\n",
+ "\n",
+ "marvel_df[mask]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "43zJ5PU2jLZD"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "## DataFrame summary statistics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Rfe9Q_fDjLZD"
+ },
+ "source": [
+ "### Show basic statistics of `marvel_df`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "A_8rxatFjLZE"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kE3LM7TAkQ5W"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "qxDPgyjSjLZE"
+ },
+ "source": [
+ "marvel_df.describe()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NW3FB_NgjLZE"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the `marvel_df` pandas DataFrame, show the mean value of `first_appearance`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "BIls1kUJjLZE"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uocq7tdVkRlG"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "X_DRrH7ljLZE"
+ },
+ "source": [
+ "\n",
+ "#np.mean(marvel_df.first_appearance)\n",
+ "marvel_df.first_appearance.mean()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vuhRS12EjLZF"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the `marvel_df` pandas DataFrame, show the min value of `first_appearance`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "viOWed-AjLZF"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EutiFPPpkSRa"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "4kSSaJijjLZF"
+ },
+ "source": [
+ "#np.min(marvel_df.first_appearance)\n",
+ "marvel_df.first_appearance.min()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "r-3bsrsHjLZF"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Given the `marvel_df` pandas DataFrame, get the characters with the min value of `first_appearance`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "G1MUq1VGjLZG"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3QDGWHEfkS3d"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "LXtprMqijLZG"
+ },
+ "source": [
+ "mask = marvel_df['first_appearance'] == marvel_df.first_appearance.min()\n",
+ "marvel_df[mask]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nTj4KLpMjLZH"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "## DataFrame basic plottings"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IgHDLgpejLZH"
+ },
+ "source": [
+ "### Reset index names of `marvel_df`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "31KAfhQPjLZH"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s8VxHKoekZub"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "-cjJwiJsjLZH"
+ },
+ "source": [
+ "marvel_df = marvel_df.reset_index()\n",
+ "\n",
+ "marvel_df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cH_iEwvijLZI"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Plot the values of `first_appearance`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kRjTn8PSjLZI"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e0LPAad8kakr"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "vHhSIsrcjLZI"
+ },
+ "source": [
+ "#plt.plot(marvel_df.index, marvel_df.first_appearance)\n",
+ "marvel_df.first_appearance.plot()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xFNkvfqIjLZI"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "### Plot a histogram (plot.hist) with values of `first_appearance`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_4VRp1x_jLZI"
+ },
+ "source": [
+ "# your code goes here\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3MFce_CEkbPS"
+ },
+ "source": [
+ "#### _Answer:_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "cell_type": "solution",
+ "id": "L0TsOCCljLZJ"
+ },
+ "source": [
+ "plt.hist(marvel_df.first_appearance)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0v3zP09cjLZJ"
+ },
+ "source": [
+ "## \n"
+ ]
+ }
+ ]
+}
\ No newline at end of file