added jeffs solution notebook

Mission304Solutions.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Python Intermediate: Creating a Baby Pandas Class Solutions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this project, we'll be building a mini-version of the Pandas class. The goal of this project, is to create a class that can perform basic Pandas functions. The questions we'll be answering in this project are: \n",
+    "    - Which song had the highest number of plays in one day?\n",
+    "    - Which song had the lowest number of plays in one day?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Specifications/Requirements\n",
+    "\n",
+    "BabyPandas should make it easy for us to load , preview, manipulate, and make calculations with our data. \n",
+    "\n",
+    "To preview our data, we’ll need to:\n",
+    "- Be able to view the first five rows\n",
+    "- Be able to view the shape of our data\n",
+    "- Be able to view the data types for each column\n",
+    "\n",
+    "To manipulate our data, we’ll need to: \n",
+    "- Add new columns\n",
+    "- Be able to apply values to columns\n",
+    "- Be able to subset our data\n",
+    "- Change the data type\n",
+    "\n",
+    "To make calculations, we’ll need to:\n",
+    "- Finding the minimum\n",
+    "- Finding the maximum\n",
+    "- Finding the mean\n",
+    "- Finding the standard deviation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Translating our words into objects\n",
+    "\n",
+    "BabyPandas -> Class\n",
+    "\n",
+    "Load -> Method\n",
+    "\n",
+    "Data -> Attribute\n",
+    "\n",
+    "Columns -> Attribute\n",
+    "\n",
+    "## Preview\n",
+    "\n",
+    "View the first five rows -> Method\n",
+    "\n",
+    "View num of rows/cols of our data -> Method\n",
+    "\n",
+    "View the data types for each column -> Method\n",
+    "\n",
+    "\n",
+    "## Manipulate\n",
+    "\n",
+    "Add new columns -> Method\n",
+    "\n",
+    "Apply values to columns -> Method\n",
+    "\n",
+    "subset our data -> Method\n",
+    "\n",
+    "Change the data type -> Method\n",
+    "\n",
+    "\n",
+    "## Calculations\n",
+    "\n",
+    "Minimum -> Method\n",
+    "\n",
+    "Maximum -> Method\n",
+    "\n",
+    "Mean -> Method\n",
+    "\n",
+    "Standard deviation -> Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "['Despacito (Featuring Daddy Yankee)', 'Luis Fonsi', 64238]\n",
+      "['Por Fin Te Encontré', 'Cali Y El Dandee', 1993]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import csv\n",
+    "from statistics import mean, stdev, median, mode\n",
+    "\n",
+    "class BabyPandas():\n",
+    "    def __init__(self, filename):\n",
+    "        self.filename = filename\n",
+    "    \n",
+    "    def read_data(self):\n",
+    "        '''\n",
+    "        Reads and opens the data\n",
+    "        '''\n",
+    "        f = open(self.filename,\"r\")\n",
+    "        self.data = list(csv.reader(f))\n",
+    "        self.columns = self.data[0]\n",
+    "    \n",
+    "    def head(self):\n",
+    "        '''\n",
+    "        Displays the first five rows\n",
+    "        '''\n",
+    "        return self.data[:5]\n",
+    "    \n",
+    "    def info(self):\n",
+    "        types = []\n",
+    "        for value in self.data[1]:\n",
+    "            types.append(type(value))\n",
+    "        return types\n",
+    "        \n",
+    "    \n",
+    "    def shape(self):\n",
+    "        num_rows = 0\n",
+    "        for row in self.data:\n",
+    "            num_rows += 1\n",
+    "        \n",
+    "        num_cols = len(self.data[0])\n",
+    "        return [num_rows, num_cols]\n",
+    "    \n",
+    "    def new_column(self, column_name):\n",
+    "        for pos, d in enumerate(self.data):\n",
+    "            if pos == 0:\n",
+    "                d.append(column_name)\n",
+    "            else:\n",
+    "                d.append('NA')\n",
+    "    \n",
+    "    def apply(self, column_name, new_value):\n",
+    "        for pos, col in enumerate(self.data[0]):\n",
+    "            if col == column_name:\n",
+    "                column_index = pos\n",
+    "        \n",
+    "        for data in self.data[1:]:\n",
+    "            data[column_index] = new_value\n",
+    "    \n",
+    "    def change_type(self, column_name, function):\n",
+    "        for pos, col in enumerate(self.data[0]):\n",
+    "            if col == column_name:\n",
+    "                column_index = pos\n",
+    "        \n",
+    "        for data in self.data[1:]:\n",
+    "            data[column_index] = function(data[column_index])\n",
+    "    \n",
+    "    def subset(self, column_name, row_value):\n",
+    "        for pos, col in enumerate(self.data[0]):\n",
+    "            if col == column_name:\n",
+    "                column_index = pos\n",
+    "        \n",
+    "        print(column_index)\n",
+    "        subset_data = []\n",
+    "        for data in self.data[1:]:\n",
+    "            if row_value in data:\n",
+    "                subset_data.append(data[column_index])\n",
+    "        return subset_data\n",
+    "\n",
+    "    \n",
+    "    def summary_stats(self, column_name):\n",
+    "        for pos, col in enumerate(self.data[0]):\n",
+    "            if col == column_name:\n",
+    "                column_index = pos\n",
+    "\n",
+    "        num_data = [data[column_index] for data in self.data[1:]]\n",
+    "        m = statistics.mean(num_data)\n",
+    "        std = stdev(num_data)\n",
+    "        median = statistics.median(num_data)\n",
+    "        \n",
+    "        print(\"Mean is {mean}\".format(mean= m))\n",
+    "        print(\"Standard Deviation is {std}\".format(std= std))\n",
+    "        print(\"Median is {median}\".format(median= median))\n",
+    "        \n",
+    "            \n",
+    "    def minimum(self, column):\n",
+    "        for pos, col in enumerate(self.data[0]):\n",
+    "            if col == column:\n",
+    "                column_index = pos\n",
+    "\n",
+    "        ## Find min value\n",
+    "        col_data = []\n",
+    "        for row in self.data[1:]:\n",
+    "            col_data.append([row[1],row[2],row[column_index]])\n",
+    "        \n",
+    "        return min(col_data, key= lambda x: x[2])\n",
+    "    \n",
+    "    def maximum(self, column):\n",
+    "        for pos, col in enumerate(self.data[0]):\n",
+    "            if col == column:\n",
+    "                column_index = pos\n",
+    "        ## Find min value\n",
+    "        col_data = []\n",
+    "        for row in self.data[1:]:\n",
+    "            col_data.append([row[1],row[2],row[column_index]])\n",
+    "        return max(col_data, key= lambda x: x[2])\n",
+    "    \n",
+    "s = BabyPandas(\"music_data.csv\")\n",
+    "s.read_data()\n",
+    "\n",
+    "s.info()\n",
+    "s.shape()\n",
+    "s.columns\n",
+    "s.new_column('hello')\n",
+    "s.change_type('Streams',int)\n",
+    "s.subset(\"Artist\",\"Shakira\")\n",
+    "print(s.maximum(\"Streams\"))\n",
+    "print(s.minimum(\"Streams\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Results\n",
+    "\n",
+    "The song that had the highest number of streams in one day was Despacito by Luis Fonsi with 64238 streams. \n",
+    "\n",
+    "The song that had the lowest number of streams in one day was Por Fin Te Encontre by Cali Y El Dandee with 1993. \n"
+   ]
+  }
+ ],
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+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
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+   "codemirror_mode": {
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