Code/merge_csv_files.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merging all CSV Files in a Directory into Single CSV Containing All Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### lets first see what csv files are in the directory.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sales_December_2019.csv\n",
      "Sales_April_2019.csv\n",
      "Sales_February_2019.csv\n",
      "Sales_March_2019.csv\n",
      "Sales_August_2019.csv\n",
      "Sales_May_2019.csv\n",
      "Sales_November_2019.csv\n",
      "Sales_October_2019.csv\n",
      "Sales_January_2019.csv\n",
      "Sales_September_2019.csv\n",
      "Sales_July_2019.csv\n",
      "Sales_June_2019.csv\n"
     ]
    }
   ],
   "source": [
    "files=[f for f in os.listdir(\"./SalesAnalysis/Sales_Data\") if f.endswith('.csv')]\n",
    "all_data = pd.DataFrame()\n",
    "\n",
    "for file in files:\n",
    "    print(file)\n",
    "    #df = pd.read_csv('./csse_covid_19_daily_reports_us/' + file)\n",
    "    #all_data = pd.concat([all_data, df])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ^ We just looked inside the directory and printed each file. Now, lets merge them into one single csv file containing all of our data.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>295665</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700</td>\n",
       "      <td>12/30/19 00:01</td>\n",
       "      <td>136 Church St, New York City, NY 10001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>295666</td>\n",
       "      <td>LG Washing Machine</td>\n",
       "      <td>1</td>\n",
       "      <td>600.0</td>\n",
       "      <td>12/29/19 07:03</td>\n",
       "      <td>562 2nd St, New York City, NY 10001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>295667</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>12/12/19 18:21</td>\n",
       "      <td>277 Main St, New York City, NY 10001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>295668</td>\n",
       "      <td>27in FHD Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>12/22/19 15:13</td>\n",
       "      <td>410 6th St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>295669</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>12/18/19 12:38</td>\n",
       "      <td>43 Hill St, Atlanta, GA 30301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID               Product Quantity Ordered Price Each      Order Date  \\\n",
       "0   295665    Macbook Pro Laptop                1       1700  12/30/19 00:01   \n",
       "1   295666    LG Washing Machine                1      600.0  12/29/19 07:03   \n",
       "2   295667  USB-C Charging Cable                1      11.95  12/12/19 18:21   \n",
       "3   295668      27in FHD Monitor                1     149.99  12/22/19 15:13   \n",
       "4   295669  USB-C Charging Cable                1      11.95  12/18/19 12:38   \n",
       "\n",
       "                         Purchase Address  \n",
       "0  136 Church St, New York City, NY 10001  \n",
       "1     562 2nd St, New York City, NY 10001  \n",
       "2    277 Main St, New York City, NY 10001  \n",
       "3     410 6th St, San Francisco, CA 94016  \n",
       "4           43 Hill St, Atlanta, GA 30301  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = pd.DataFrame()\n",
    "for file in files:\n",
    "    df = pd.read_csv('././SalesAnalysis/Sales_Data/' + file)\n",
    "    all_data = pd.concat([all_data, df])\n",
    "\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We just compressed each csv files' contents into a single csv and can now be used for analysis. You can verify that all contents were transferred by saving the new data frame ( in this case 'all_data') as a csv file using pd.to_csv('file_name') and then accessing it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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