From cf0edc87eb27f847a61557a4115f4d4447f6d72e Mon Sep 17 00:00:00 2001 From: Melanie Shimano Date: Mon, 14 May 2018 15:30:38 -0400 Subject: [PATCH 1/2] Made in Baltimore Popup Shop Analysis --- ...e in Baltimore - Popup Shop Analysis.ipynb | 9045 ++++++++++++++--- 1 file changed, 7543 insertions(+), 1502 deletions(-) diff --git a/March2018_Meetup/Made in Baltimore - Popup Shop Analysis.ipynb b/March2018_Meetup/Made in Baltimore - Popup Shop Analysis.ipynb index dfcecb1..e78f36c 100644 --- a/March2018_Meetup/Made in Baltimore - Popup Shop Analysis.ipynb +++ b/March2018_Meetup/Made in Baltimore - Popup Shop Analysis.ipynb @@ -2,61 +2,191 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": 0, + "height": 2, + "hidden": false, + "row": 0, + "width": 6 + }, + "report_default": { + "hidden": false + } + } + } + } + }, "source": [ "# Made in Baltimore Holiday Shop Analysis" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": 6, + "height": 2, + "hidden": false, + "row": 0, + "width": 6 + }, + "report_default": { + "hidden": false + } + } + } + } + }, "source": [ "Looking at the Made in Baltimore Holiday Popup Shop transaction data in November-December 2017. More info at https://madeinbaltimore.org/holidaystore/" ] }, { - "cell_type": "code", - "execution_count": 1, + "cell_type": "markdown", "metadata": { - "collapsed": true + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": 8, + "height": 4, + "hidden": true, + "row": 0, + "width": 4 + }, + "report_default": { + "hidden": true + } + } + } + } }, - "outputs": [], "source": [ - "import pandas as pd\n", - "import datetime as dt" + "Import the data and read in the dataset with pandas" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } + }, + "outputs": [], "source": [ - "Import the data and read in the dataset with pandas" + "import pandas as pd" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": { - "collapsed": true + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } }, "outputs": [], "source": [ - "url = 'https://raw.githubusercontent.com/melanieshimano/python4CityGovtProcessImprovement/master/March2017_Meetup/MadeInBaltimore_data.csv'" + "url = 'https://raw.githubusercontent.com/melanieshimano/python4CityGovtProcessImprovement/master/March2018_Meetup/MadeInBaltimore_data.csv'" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "execution_count": 7, + "metadata": { + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } + }, "outputs": [], "source": [ "df= pd.read_csv(url)" ] }, + { + "cell_type": "markdown", + "metadata": { + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": {}, + "report_default": { + "hidden": false + } + } + } + } + }, + "source": [ + "# The Data" + ] + }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 36, + "metadata": { + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": 0, + "height": 10, + "hidden": false, + "row": 2, + "width": 12 + }, + "report_default": { + "hidden": false + } + } + } + } + }, "outputs": [ { "data": { @@ -79,830 +209,625 @@ " \n", " \n", " \n", - " Date/Time\n", " Store_ID\n", - " Unnamed: 2\n", - " Unnamed: 3\n", " Inv #\n", - " Unnamed: 5\n", - " Cust #\n", - " Unnamed: 7\n", " Cashier ID\n", " PM\n", - " ...\n", + " Total Cost\n", + " Total Price\n", " Tax1\n", - " Tax2\n", - " Tax3\n", - " Tax4\n", - " Tax5\n", - " Tax6\n", - " Unnamed: 19\n", " GTotal\n", " Gross\n", - " Unnamed: 22\n", + " Date\n", + " hour\n", + " day_number\n", + " day_name\n", + " shop_date\n", + " shop_date_total\n", + " day_total\n", + " hour_total\n", " \n", " \n", " \n", " \n", - " 0\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - 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" 11/25/2017 12:09: PM\n", " 1001.0\n", - " NaN\n", - " NaN\n", " 130.0\n", - " 101.0\n", - " NaN\n", - " NaN\n", " 100101.0\n", " CA\n", - " ...\n", + " 0.00\n", + " 40.00\n", " 2.40\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " NaN\n", " 42.40\n", - " 40.0\n", - " NaN\n", + " 40.00\n", + " 2017-11-25 12:09:00\n", + " 12\n", + " 5\n", + " Saturday\n", + " 2017-11-25\n", + " 861.00\n", + " 12939.23\n", + " 6478.18\n", " \n", " \n", " 4\n", - " 11/25/2017 12:23: PM\n", " 1001.0\n", - " NaN\n", - " NaN\n", " 131.0\n", - " 101.0\n", - " NaN\n", - " NaN\n", " 100101.0\n", " CA\n", - " ...\n", + " 0.00\n", + " 5.50\n", " 0.33\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " NaN\n", " 5.83\n", - " 5.5\n", - " NaN\n", - " \n", - " \n", - "\n", - "

5 rows × 23 columns

\n", - "" - ], - "text/plain": [ - " Date/Time Store_ID Unnamed: 2 Unnamed: 3 Inv # \\\n", - "0 NaN NaN NaN NaN NaN \n", - "1 11/25/2017 11:58: AM 1001.0 NaN NaN 128.0 \n", - "2 11/25/2017 12:08: PM 1001.0 NaN NaN 129.0 \n", - "3 11/25/2017 12:09: PM 1001.0 NaN NaN 130.0 \n", - "4 11/25/2017 12:23: PM 1001.0 NaN NaN 131.0 \n", - "\n", - " Unnamed: 5 Cust # Unnamed: 7 Cashier ID PM ... Tax1 Tax2 \\\n", - "0 NaN NaN NaN NaN NaN ... NaN NaN \n", - "1 101.0 NaN NaN 100101.0 CC ... 2.52 0.0 \n", - "2 101.0 NaN NaN 100101.0 CC ... 1.20 0.0 \n", - "3 101.0 NaN NaN 100101.0 CA ... 2.40 0.0 \n", - "4 101.0 NaN NaN 100101.0 CA ... 0.33 0.0 \n", - "\n", - " Tax3 Tax4 Tax5 Tax6 Unnamed: 19 GTotal Gross Unnamed: 22 \n", - "0 NaN NaN NaN NaN NaN NaN NaN NaN \n", - "1 0.0 0.0 0.0 0.0 NaN 44.52 42.0 NaN \n", - "2 0.0 0.0 0.0 0.0 NaN 21.20 20.0 NaN \n", - "3 0.0 0.0 0.0 0.0 NaN 42.40 40.0 NaN \n", - "4 0.0 0.0 0.0 0.0 NaN 5.83 5.5 NaN \n", - "\n", - "[5 rows x 23 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Clean the Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I edited the original excel sheet to reformat the data because there were daily totals inline with the transaction data. Although fairly straightforward, there were still some empty columns and rows once I imported into this notebook, so we will need to clean the dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#drop columns that are all NaN\n", - "col = [2,3,6,7,12,19, 22] #column numbers are found from the column header \"Unnamed: #\"\n", - "df= df.drop(df.columns[[col]], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Date/TimeStore_IDInv #Unnamed: 5Cashier IDPMTotal CostTotal PriceTax1Tax2Tax3Tax4Tax5Tax6GTotalGross
0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
111/25/2017 11:58: AM1001.0128.0101.0100101.0CC0.0042.002.520.00.00.00.00.044.5242.00
211/25/2017 12:08: PM1001.0129.0101.0100101.0CC0.0020.001.200.00.00.00.00.021.2020.00
311/25/2017 12:09: PM1001.0130.0101.0100101.0CA0.0040.002.400.00.00.00.00.042.4040.00
411/25/2017 12:23: PM1001.0131.0101.0100101.0CA0.005.500.330.00.00.00.00.05.835.505.502017-11-25 12:23:00125Saturday2017-11-25861.0012939.236478.18
511/25/2017 12:49: PM1001.0132.0101.0100101.0CC0.006.500.390.00.00.00.00.06.896.502017-11-25 12:49:00125Saturday2017-11-25861.0012939.236478.18
611/25/2017 12:56: PM1001.0133.0101.0100101.0CC0.0030.501.830.00.00.00.00.032.3330.502017-11-25 12:56:00125Saturday2017-11-25861.0012939.236478.18
711/25/2017 1:45: PM1001.0134.0101.0100101.0CA0.0035.002.100.00.00.00.00.037.1035.002017-11-25 13:45:00135Saturday2017-11-25861.0012939.236853.70
811/25/2017 1:59: PM1001.0135.0101.0100101.0CC0.0052.503.150.00.00.00.00.055.6552.502017-11-25 13:59:00135Saturday2017-11-25861.0012939.236853.70
911/25/2017 2:00: PM1001.0136.0101.0100101.0CA0.006.500.390.00.00.00.00.06.896.502017-11-25 14:00:00145Saturday2017-11-25861.0012939.234886.87
1011/25/2017 2:25: PM1001.0137.0101.0100101.0CC0.0015.000.900.00.00.00.00.015.9015.002017-11-25 14:25:00145Saturday2017-11-25861.0012939.234886.87
1111/25/2017 2:32: PM1001.0138.0101.0100101.0CC0.0013.000.780.00.00.00.00.013.7813.002017-11-25 14:32:00145Saturday2017-11-25861.0012939.234886.87
1211/25/2017 2:36: PM1001.0139.0101.0100101.0CC0.0028.001.680.00.00.00.00.029.6828.002017-11-25 14:36:00145Saturday2017-11-25861.0012939.234886.87
1311/25/2017 2:37: PM1001.0140.0101.0100101.0CA0.0037.002.220.00.00.00.00.039.2237.002017-11-25 14:37:00145Saturday2017-11-25861.0012939.234886.87
1411/25/2017 2:39: PM1001.0141.0101.0100101.0CA0.005.500.330.00.00.00.00.05.835.502017-11-25 14:39:00145Saturday2017-11-25861.0012939.234886.87
1511/25/2017 2:41: PM1001.0142.0101.0100101.0CA0.0035.002.100.00.00.00.00.037.1035.002017-11-25 14:41:00145Saturday2017-11-25861.0012939.234886.87
1611/25/2017 2:42: PM1001.0143.0101.0100101.0CA0.0025.001.500.00.00.00.00.026.5025.002017-11-25 14:42:00145Saturday2017-11-25861.0012939.234886.87
1711/25/2017 2:44: PM1001.0144.0101.0100101.0CC0.0011.000.660.00.00.00.00.011.6611.002017-11-25 14:44:00145Saturday2017-11-25861.0012939.234886.87
1811/25/2017 2:47: PM1001.0145.0101.0100101.0CC0.0046.502.790.00.00.00.00.049.2946.502017-11-25 14:47:00145Saturday2017-11-25861.0012939.234886.87
1911/25/2017 2:48: PM1001.0146.0101.0100101.0CA0.0015.000.900.00.00.00.00.015.9015.002017-11-25 14:48:00145Saturday2017-11-25861.0012939.234886.87
2011/25/2017 3:09: PM1001.0147.0101.0100101.0CA0.0030.001.800.00.00.00.00.031.8030.002017-11-25 15:09:00155Saturday2017-11-25861.0012939.235244.41
2111/25/2017 3:35: PM1001.0148.0101.0100101.0CC0.0063.003.780.00.00.00.00.066.7863.002017-11-25 15:35:00155Saturday2017-11-25861.0012939.235244.41
2211/25/2017 3:41: PM1001.0149.0101.0100101.0CC0.0036.002.160.00.00.00.00.038.1636.002017-11-25 15:41:00155Saturday2017-11-25861.0012939.235244.41
2311/25/2017 3:45: PM1001.0150.0101.0100101.0CC0.0042.002.520.00.00.00.00.044.5242.002017-11-25 15:45:00155Saturday2017-11-25861.0012939.235244.41
2411/25/2017 3:47: PM1001.0151.0101.0100101.0CC0.0030.501.830.00.00.00.00.032.3330.502017-11-25 15:47:00155Saturday2017-11-25861.0012939.235244.41
2511/25/2017 3:54: PM1001.0152.0101.0100101.0CA0.0012.000.720.00.00.00.00.012.7212.002017-11-25 15:54:00155Saturday2017-11-25861.0012939.235244.41
2611/25/2017 3:56: PM1001.0153.0101.0100101.0CC0.0016.500.990.00.00.00.00.017.4916.502017-11-25 15:56:00155Saturday2017-11-25861.0012939.235244.41
2711/25/2017 4:04: PM1001.0154.0101.0100101.0CC0.0030.001.800.00.00.00.00.031.8030.002017-11-25 16:04:00165Saturday2017-11-25861.0012939.234032.34
2811/25/2017 4:51: PM1001.0155.0101.0100101.0CC0.0037.002.220.00.00.00.00.039.2237.002017-11-25 16:51:00165Saturday2017-11-25861.0012939.234032.34
2911/25/2017 5:03: PM1001.0156.0101.0100101.0CC0.006.500.390.00.00.00.00.06.896.502017-11-25 17:03:00175Saturday2017-11-25861.0012939.233077.73
301001.0157.0100101.0CC0.0048.002.8850.8848.002017-11-25 17:05:00175Saturday2017-11-25861.0012939.233077.73
...............
10391001.01154.0100101.0CC0.0010.000.6010.6010.002017-12-24 11:50:00116Sunday2017-12-24948.3312528.684350.93
10401001.01155.0100101.0CC14.8815.000.9015.900.122017-12-24 11:54:00116Sunday2017-12-24948.3312528.684350.93
10411001.01156.0100101.0CC3.578.000.488.484.432017-12-24 11:55:00116Sunday2017-12-24948.3312528.684350.93
10421001.01157.0100101.0CA61.00163.509.81173.31102.502017-12-24 12:01:00126Sunday2017-12-24948.3312528.686478.18
104312/24/2017 12:10: PM1001.01158.0101.0100101.0CA0.0026.001.560.00.00.00.00.027.5626.002017-12-24 12:10:00126Sunday2017-12-24948.3312528.686478.18
104412/24/2017 12:15: PM1001.01159.0101.0100101.0CA20.7933.502.010.00.00.00.00.035.5112.712017-12-24 12:15:00126Sunday2017-12-24948.3312528.686478.18
104512/24/2017 12:18: PM1001.01160.0101.0100101.0CC23.2839.982.400.00.00.00.00.042.3816.702017-12-24 12:18:00126Sunday2017-12-24948.3312528.686478.18
104612/24/2017 12:20: PM1001.01161.0101.0100101.0CC12.0022.001.320.00.00.00.00.023.3210.002017-12-24 12:20:00126Sunday2017-12-24948.3312528.686478.18
104712/24/2017 12:39: PM1001.01162.0101.0100101.0CA14.8815.000.900.00.00.00.00.015.900.122017-12-24 12:39:00126Sunday2017-12-24948.3312528.686478.18
104812/24/2017 12:53: PM1001.01163.0101.0100101.0CC20.00120.007.200.00.00.00.00.0127.20100.002017-12-24 12:53:00126Sunday2017-12-24948.3312528.686478.18
104912/24/2017 12:55: PM1001.01164.0101.0100101.0CC5.1535.502.130.00.00.00.00.037.6330.352017-12-24 12:55:00126Sunday2017-12-24948.3312528.686478.18
105012/24/2017 1:04: PM1001.01165.0101.0100101.0CC33.5738.002.280.00.00.00.00.040.284.432017-12-24 13:04:00136Sunday2017-12-24948.3312528.686853.70
105112/24/2017 1:10: PM1001.01166.0101.0100101.0CC12.0025.991.560.00.00.00.00.027.5513.992017-12-24 13:10:00136Sunday2017-12-24948.3312528.686853.70
105212/24/2017 1:11: PM1001.01167.0101.0100101.0CC11.6735.002.100.00.00.00.00.037.1023.33
2017-12-24 13:11:00136Sunday2017-12-24948.3312528.686853.70
105312/24/2017 1:18: PM1001.01168.0101.0100101.0CC0.0060.003.600.00.00.00.00.063.6060.002017-12-24 13:18:00136Sunday2017-12-24948.3312528.686853.70
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1073 rows × 16 columns

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1055 rows × 17 columns

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" ], "text/plain": [ - " Date/Time Store_ID Inv # Unnamed: 5 Cashier ID PM \\\n", - "0 NaN NaN NaN NaN NaN NaN \n", - "1 11/25/2017 11:58: AM 1001.0 128.0 101.0 100101.0 CC \n", - "2 11/25/2017 12:08: PM 1001.0 129.0 101.0 100101.0 CC \n", - "3 11/25/2017 12:09: PM 1001.0 130.0 101.0 100101.0 CA \n", - "4 11/25/2017 12:23: PM 1001.0 131.0 101.0 100101.0 CA \n", - "5 11/25/2017 12:49: PM 1001.0 132.0 101.0 100101.0 CC \n", - "6 11/25/2017 12:56: PM 1001.0 133.0 101.0 100101.0 CC \n", - "7 11/25/2017 1:45: PM 1001.0 134.0 101.0 100101.0 CA \n", - "8 11/25/2017 1:59: PM 1001.0 135.0 101.0 100101.0 CC \n", - "9 11/25/2017 2:00: PM 1001.0 136.0 101.0 100101.0 CA \n", - "10 11/25/2017 2:25: PM 1001.0 137.0 101.0 100101.0 CC \n", - "11 11/25/2017 2:32: PM 1001.0 138.0 101.0 100101.0 CC \n", - "12 11/25/2017 2:36: PM 1001.0 139.0 101.0 100101.0 CC \n", - "13 11/25/2017 2:37: PM 1001.0 140.0 101.0 100101.0 CA \n", - "14 11/25/2017 2:39: PM 1001.0 141.0 101.0 100101.0 CA \n", - "15 11/25/2017 2:41: PM 1001.0 142.0 101.0 100101.0 CA \n", - "16 11/25/2017 2:42: PM 1001.0 143.0 101.0 100101.0 CA \n", - "17 11/25/2017 2:44: PM 1001.0 144.0 101.0 100101.0 CC \n", - "18 11/25/2017 2:47: PM 1001.0 145.0 101.0 100101.0 CC \n", - "19 11/25/2017 2:48: PM 1001.0 146.0 101.0 100101.0 CA \n", - "20 11/25/2017 3:09: PM 1001.0 147.0 101.0 100101.0 CA \n", - "21 11/25/2017 3:35: PM 1001.0 148.0 101.0 100101.0 CC \n", - "22 11/25/2017 3:41: PM 1001.0 149.0 101.0 100101.0 CC \n", - "23 11/25/2017 3:45: PM 1001.0 150.0 101.0 100101.0 CC \n", - "24 11/25/2017 3:47: PM 1001.0 151.0 101.0 100101.0 CC \n", - "25 11/25/2017 3:54: PM 1001.0 152.0 101.0 100101.0 CA \n", - "26 11/25/2017 3:56: PM 1001.0 153.0 101.0 100101.0 CC \n", - "27 11/25/2017 4:04: PM 1001.0 154.0 101.0 100101.0 CC \n", - "28 11/25/2017 4:51: PM 1001.0 155.0 101.0 100101.0 CC \n", - "29 11/25/2017 5:03: PM 1001.0 156.0 101.0 100101.0 CC \n", - "... ... ... ... ... ... ... \n", - "1043 12/24/2017 12:10: PM 1001.0 1158.0 101.0 100101.0 CA \n", - "1044 12/24/2017 12:15: PM 1001.0 1159.0 101.0 100101.0 CA \n", - "1045 12/24/2017 12:18: PM 1001.0 1160.0 101.0 100101.0 CC \n", - "1046 12/24/2017 12:20: PM 1001.0 1161.0 101.0 100101.0 CC \n", - "1047 12/24/2017 12:39: PM 1001.0 1162.0 101.0 100101.0 CA \n", - "1048 12/24/2017 12:53: PM 1001.0 1163.0 101.0 100101.0 CC \n", - "1049 12/24/2017 12:55: PM 1001.0 1164.0 101.0 100101.0 CC \n", - "1050 12/24/2017 1:04: PM 1001.0 1165.0 101.0 100101.0 CC \n", - "1051 12/24/2017 1:10: PM 1001.0 1166.0 101.0 100101.0 CC \n", - "1052 12/24/2017 1:11: PM 1001.0 1167.0 101.0 100101.0 CC \n", - "1053 12/24/2017 1:18: PM 1001.0 1168.0 101.0 100101.0 CC \n", - "1054 12/24/2017 1:24: PM 1001.0 1169.0 101.0 100101.0 CC \n", - "1055 12/24/2017 1:35: PM 1001.0 1170.0 101.0 100101.0 CC \n", - "1056 12/24/2017 1:49: PM 1001.0 1171.0 101.0 100101.0 CC \n", - "1057 12/24/2017 1:50: PM 1001.0 1172.0 101.0 100101.0 CA \n", - "1058 12/24/2017 1:58: PM 1001.0 1173.0 101.0 100101.0 CA \n", - "1059 12/24/2017 2:00: PM 1001.0 1174.0 101.0 100101.0 CC \n", - "1060 12/24/2017 2:11: PM 1001.0 1175.0 101.0 100101.0 CC \n", - "1061 12/24/2017 2:35: PM 1001.0 1176.0 101.0 100101.0 CC \n", - "1062 12/24/2017 2:50: PM 1001.0 1177.0 101.0 100101.0 CC \n", - "1063 12/24/2017 3:12: PM 1001.0 1178.0 101.0 100101.0 CA \n", - "1064 12/24/2017 3:21: PM 1001.0 1179.0 101.0 100101.0 CC \n", - "1065 12/24/2017 4:08: PM 1001.0 1180.0 101.0 100101.0 CA \n", - "1066 12/24/2017 4:17: PM 1001.0 1181.0 101.0 100101.0 CC \n", - "1067 12/24/2017 5:03: PM 1001.0 1182.0 101.0 100101.0 CA \n", - "1068 12/24/2017 5:18: PM 1001.0 1183.0 101.0 100101.0 CC \n", - "1069 NaN NaN NaN NaN NaN NaN \n", - "1070 NaN NaN NaN NaN NaN NaN \n", - "1071 NaN NaN NaN NaN NaN NaN \n", - "1072 NaN NaN NaN NaN NaN NaN \n", + " Store_ID Inv # Cashier ID PM Total Cost Total Price Tax1 \\\n", + "1 1001.0 128.0 100101.0 CC 0.00 42.00 2.52 \n", + "2 1001.0 129.0 100101.0 CC 0.00 20.00 1.20 \n", + "3 1001.0 130.0 100101.0 CA 0.00 40.00 2.40 \n", + "4 1001.0 131.0 100101.0 CA 0.00 5.50 0.33 \n", + "5 1001.0 132.0 100101.0 CC 0.00 6.50 0.39 \n", + "6 1001.0 133.0 100101.0 CC 0.00 30.50 1.83 \n", + "7 1001.0 134.0 100101.0 CA 0.00 35.00 2.10 \n", + "8 1001.0 135.0 100101.0 CC 0.00 52.50 3.15 \n", + "9 1001.0 136.0 100101.0 CA 0.00 6.50 0.39 \n", + "10 1001.0 137.0 100101.0 CC 0.00 15.00 0.90 \n", + "11 1001.0 138.0 100101.0 CC 0.00 13.00 0.78 \n", + "12 1001.0 139.0 100101.0 CC 0.00 28.00 1.68 \n", + "13 1001.0 140.0 100101.0 CA 0.00 37.00 2.22 \n", + "14 1001.0 141.0 100101.0 CA 0.00 5.50 0.33 \n", + "15 1001.0 142.0 100101.0 CA 0.00 35.00 2.10 \n", + "16 1001.0 143.0 100101.0 CA 0.00 25.00 1.50 \n", + "17 1001.0 144.0 100101.0 CC 0.00 11.00 0.66 \n", + "18 1001.0 145.0 100101.0 CC 0.00 46.50 2.79 \n", + "19 1001.0 146.0 100101.0 CA 0.00 15.00 0.90 \n", + "20 1001.0 147.0 100101.0 CA 0.00 30.00 1.80 \n", + "21 1001.0 148.0 100101.0 CC 0.00 63.00 3.78 \n", + "22 1001.0 149.0 100101.0 CC 0.00 36.00 2.16 \n", + "23 1001.0 150.0 100101.0 CC 0.00 42.00 2.52 \n", + "24 1001.0 151.0 100101.0 CC 0.00 30.50 1.83 \n", + "25 1001.0 152.0 100101.0 CA 0.00 12.00 0.72 \n", + "26 1001.0 153.0 100101.0 CC 0.00 16.50 0.99 \n", + "27 1001.0 154.0 100101.0 CC 0.00 30.00 1.80 \n", + "28 1001.0 155.0 100101.0 CC 0.00 37.00 2.22 \n", + "29 1001.0 156.0 100101.0 CC 0.00 6.50 0.39 \n", + "30 1001.0 157.0 100101.0 CC 0.00 48.00 2.88 \n", + "... ... ... ... .. ... ... ... \n", + "1039 1001.0 1154.0 100101.0 CC 0.00 10.00 0.60 \n", + "1040 1001.0 1155.0 100101.0 CC 14.88 15.00 0.90 \n", + "1041 1001.0 1156.0 100101.0 CC 3.57 8.00 0.48 \n", + "1042 1001.0 1157.0 100101.0 CA 61.00 163.50 9.81 \n", + "1043 1001.0 1158.0 100101.0 CA 0.00 26.00 1.56 \n", + "1044 1001.0 1159.0 100101.0 CA 20.79 33.50 2.01 \n", + "1045 1001.0 1160.0 100101.0 CC 23.28 39.98 2.40 \n", + "1046 1001.0 1161.0 100101.0 CC 12.00 22.00 1.32 \n", + "1047 1001.0 1162.0 100101.0 CA 14.88 15.00 0.90 \n", + "1048 1001.0 1163.0 100101.0 CC 20.00 120.00 7.20 \n", + "1049 1001.0 1164.0 100101.0 CC 5.15 35.50 2.13 \n", + "1050 1001.0 1165.0 100101.0 CC 33.57 38.00 2.28 \n", + "1051 1001.0 1166.0 100101.0 CC 12.00 25.99 1.56 \n", + "1052 1001.0 1167.0 100101.0 CC 11.67 35.00 2.10 \n", + "1053 1001.0 1168.0 100101.0 CC 0.00 60.00 3.60 \n", + "1054 1001.0 1169.0 100101.0 CC 65.71 110.00 6.60 \n", + "1055 1001.0 1170.0 100101.0 CC 25.00 40.00 2.40 \n", + "1056 1001.0 1171.0 100101.0 CC 43.67 183.00 10.98 \n", + "1057 1001.0 1172.0 100101.0 CA 5.00 20.00 1.20 \n", + "1058 1001.0 1173.0 100101.0 CA 0.00 10.00 0.60 \n", + "1059 1001.0 1174.0 100101.0 CC 1.82 13.00 0.78 \n", + "1060 1001.0 1175.0 100101.0 CC 37.50 49.00 2.94 \n", + "1061 1001.0 1176.0 100101.0 CC 39.73 116.50 6.99 \n", + "1062 1001.0 1177.0 100101.0 CC 5.50 5.50 0.33 \n", + "1063 1001.0 1178.0 100101.0 CA 0.00 5.00 0.30 \n", + "1064 1001.0 1179.0 100101.0 CC 0.00 95.00 5.70 \n", + "1065 1001.0 1180.0 100101.0 CA 1.93 18.00 1.08 \n", + "1066 1001.0 1181.0 100101.0 CC 0.00 30.00 1.80 \n", + "1067 1001.0 1182.0 100101.0 CA 0.00 15.00 0.90 \n", + "1068 1001.0 1183.0 100101.0 CC 117.49 127.00 7.62 \n", "\n", - " Total Cost Total Price Tax1 Tax2 Tax3 Tax4 Tax5 Tax6 GTotal \\\n", - "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", - "1 0.00 42.00 2.52 0.0 0.0 0.0 0.0 0.0 44.52 \n", - "2 0.00 20.00 1.20 0.0 0.0 0.0 0.0 0.0 21.20 \n", - "3 0.00 40.00 2.40 0.0 0.0 0.0 0.0 0.0 42.40 \n", - "4 0.00 5.50 0.33 0.0 0.0 0.0 0.0 0.0 5.83 \n", - "5 0.00 6.50 0.39 0.0 0.0 0.0 0.0 0.0 6.89 \n", - "6 0.00 30.50 1.83 0.0 0.0 0.0 0.0 0.0 32.33 \n", - "7 0.00 35.00 2.10 0.0 0.0 0.0 0.0 0.0 37.10 \n", - "8 0.00 52.50 3.15 0.0 0.0 0.0 0.0 0.0 55.65 \n", - "9 0.00 6.50 0.39 0.0 0.0 0.0 0.0 0.0 6.89 \n", - "10 0.00 15.00 0.90 0.0 0.0 0.0 0.0 0.0 15.90 \n", - "11 0.00 13.00 0.78 0.0 0.0 0.0 0.0 0.0 13.78 \n", - "12 0.00 28.00 1.68 0.0 0.0 0.0 0.0 0.0 29.68 \n", - "13 0.00 37.00 2.22 0.0 0.0 0.0 0.0 0.0 39.22 \n", - "14 0.00 5.50 0.33 0.0 0.0 0.0 0.0 0.0 5.83 \n", - "15 0.00 35.00 2.10 0.0 0.0 0.0 0.0 0.0 37.10 \n", - "16 0.00 25.00 1.50 0.0 0.0 0.0 0.0 0.0 26.50 \n", - "17 0.00 11.00 0.66 0.0 0.0 0.0 0.0 0.0 11.66 \n", - "18 0.00 46.50 2.79 0.0 0.0 0.0 0.0 0.0 49.29 \n", - "19 0.00 15.00 0.90 0.0 0.0 0.0 0.0 0.0 15.90 \n", - "20 0.00 30.00 1.80 0.0 0.0 0.0 0.0 0.0 31.80 \n", - "21 0.00 63.00 3.78 0.0 0.0 0.0 0.0 0.0 66.78 \n", - "22 0.00 36.00 2.16 0.0 0.0 0.0 0.0 0.0 38.16 \n", - "23 0.00 42.00 2.52 0.0 0.0 0.0 0.0 0.0 44.52 \n", - "24 0.00 30.50 1.83 0.0 0.0 0.0 0.0 0.0 32.33 \n", - "25 0.00 12.00 0.72 0.0 0.0 0.0 0.0 0.0 12.72 \n", - "26 0.00 16.50 0.99 0.0 0.0 0.0 0.0 0.0 17.49 \n", - "27 0.00 30.00 1.80 0.0 0.0 0.0 0.0 0.0 31.80 \n", - "28 0.00 37.00 2.22 0.0 0.0 0.0 0.0 0.0 39.22 \n", - "29 0.00 6.50 0.39 0.0 0.0 0.0 0.0 0.0 6.89 \n", - "... ... ... ... ... ... ... ... ... ... \n", - "1043 0.00 26.00 1.56 0.0 0.0 0.0 0.0 0.0 27.56 \n", - "1044 20.79 33.50 2.01 0.0 0.0 0.0 0.0 0.0 35.51 \n", - "1045 23.28 39.98 2.40 0.0 0.0 0.0 0.0 0.0 42.38 \n", - "1046 12.00 22.00 1.32 0.0 0.0 0.0 0.0 0.0 23.32 \n", - "1047 14.88 15.00 0.90 0.0 0.0 0.0 0.0 0.0 15.90 \n", - "1048 20.00 120.00 7.20 0.0 0.0 0.0 0.0 0.0 127.20 \n", - 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"1069 NaN \n", - "1070 NaN \n", - "1071 NaN \n", - "1072 NaN \n", + " shop_date shop_date_total day_total hour_total \n", + "1 2017-11-25 861.00 12939.23 4350.93 \n", + "2 2017-11-25 861.00 12939.23 6478.18 \n", + "3 2017-11-25 861.00 12939.23 6478.18 \n", + "4 2017-11-25 861.00 12939.23 6478.18 \n", + "5 2017-11-25 861.00 12939.23 6478.18 \n", + "6 2017-11-25 861.00 12939.23 6478.18 \n", + "7 2017-11-25 861.00 12939.23 6853.70 \n", + "8 2017-11-25 861.00 12939.23 6853.70 \n", + "9 2017-11-25 861.00 12939.23 4886.87 \n", + "10 2017-11-25 861.00 12939.23 4886.87 \n", + "11 2017-11-25 861.00 12939.23 4886.87 \n", + "12 2017-11-25 861.00 12939.23 4886.87 \n", + "13 2017-11-25 861.00 12939.23 4886.87 \n", + "14 2017-11-25 861.00 12939.23 4886.87 \n", + "15 2017-11-25 861.00 12939.23 4886.87 \n", + "16 2017-11-25 861.00 12939.23 4886.87 \n", + "17 2017-11-25 861.00 12939.23 4886.87 \n", + "18 2017-11-25 861.00 12939.23 4886.87 \n", + "19 2017-11-25 861.00 12939.23 4886.87 \n", + "20 2017-11-25 861.00 12939.23 5244.41 \n", + "21 2017-11-25 861.00 12939.23 5244.41 \n", + "22 2017-11-25 861.00 12939.23 5244.41 \n", + "23 2017-11-25 861.00 12939.23 5244.41 \n", + "24 2017-11-25 861.00 12939.23 5244.41 \n", + "25 2017-11-25 861.00 12939.23 5244.41 \n", + "26 2017-11-25 861.00 12939.23 5244.41 \n", + "27 2017-11-25 861.00 12939.23 4032.34 \n", + "28 2017-11-25 861.00 12939.23 4032.34 \n", + "29 2017-11-25 861.00 12939.23 3077.73 \n", + "30 2017-11-25 861.00 12939.23 3077.73 \n", + "... ... ... ... ... \n", + "1039 2017-12-24 948.33 12528.68 4350.93 \n", + "1040 2017-12-24 948.33 12528.68 4350.93 \n", + "1041 2017-12-24 948.33 12528.68 4350.93 \n", + "1042 2017-12-24 948.33 12528.68 6478.18 \n", + "1043 2017-12-24 948.33 12528.68 6478.18 \n", + "1044 2017-12-24 948.33 12528.68 6478.18 \n", + "1045 2017-12-24 948.33 12528.68 6478.18 \n", + "1046 2017-12-24 948.33 12528.68 6478.18 \n", + "1047 2017-12-24 948.33 12528.68 6478.18 \n", + "1048 2017-12-24 948.33 12528.68 6478.18 \n", + "1049 2017-12-24 948.33 12528.68 6478.18 \n", + "1050 2017-12-24 948.33 12528.68 6853.70 \n", + "1051 2017-12-24 948.33 12528.68 6853.70 \n", + "1052 2017-12-24 948.33 12528.68 6853.70 \n", + "1053 2017-12-24 948.33 12528.68 6853.70 \n", + "1054 2017-12-24 948.33 12528.68 6853.70 \n", + "1055 2017-12-24 948.33 12528.68 6853.70 \n", + "1056 2017-12-24 948.33 12528.68 6853.70 \n", + "1057 2017-12-24 948.33 12528.68 6853.70 \n", + "1058 2017-12-24 948.33 12528.68 6853.70 \n", + "1059 2017-12-24 948.33 12528.68 4886.87 \n", + "1060 2017-12-24 948.33 12528.68 4886.87 \n", + "1061 2017-12-24 948.33 12528.68 4886.87 \n", + "1062 2017-12-24 948.33 12528.68 4886.87 \n", + "1063 2017-12-24 948.33 12528.68 5244.41 \n", + "1064 2017-12-24 948.33 12528.68 5244.41 \n", + "1065 2017-12-24 948.33 12528.68 4032.34 \n", + "1066 2017-12-24 948.33 12528.68 4032.34 \n", + "1067 2017-12-24 948.33 12528.68 3077.73 \n", + "1068 2017-12-24 948.33 12528.68 3077.73 \n", "\n", - "[1073 rows x 16 columns]" + "[1055 rows x 17 columns]" ] }, - "execution_count": 6, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1701,24 +1657,321 @@ ] }, { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "metadata": { + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": 0, + "height": 4, + "hidden": true, + "row": 14, + "width": 4 + }, + "report_default": { + "hidden": true + } + } + } + } + }, "source": [ - "#drop rows that have an NaN\n", - "df=df.dropna()" + "# Clean the Data" ] }, { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", + "cell_type": "markdown", + "metadata": { + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": 0, + "height": 4, + "hidden": true, + "row": 20, + "width": 12 + }, + "report_default": { + "hidden": true + } + } + } + } + }, + "source": [ + "I edited the original excel sheet to reformat the data because there were daily totals inline with the transaction data. Although fairly straightforward, there were still some empty columns and rows once I imported into this notebook, so we will need to clean the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } + }, + "outputs": [], + "source": [ + "#drop columns that are all NaN\n", + "col = [2,3,6,7,12,19, 22] #column numbers are found from the column header \"Unnamed: #\"\n", + "df= df.drop(df.columns[[col]], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": 0, + "height": 9, + "hidden": true, + "row": 20, + "width": 9 + }, + "report_default": { + "hidden": true + } + } + } + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" - ], - "text/plain": [ - " Date/Time Store_ID Inv # Unnamed: 5 Cashier ID PM \\\n", - "0 NaN NaN NaN NaN NaN NaN \n", - "1 11/25/2017 11:58: AM 1001.0 128.0 101.0 100101.0 CC \n", - "2 11/25/2017 12:08: PM 1001.0 129.0 101.0 100101.0 CC \n", - "3 11/25/2017 12:09: PM 1001.0 130.0 101.0 100101.0 CA \n", - "4 11/25/2017 12:23: PM 1001.0 131.0 101.0 100101.0 CA \n", - "\n", - " Total Cost Total Price Tax1 Tax2 Tax3 Tax4 Tax5 Tax6 GTotal Gross \n", - "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", - "1 0.0 42.0 2.52 0.0 0.0 0.0 0.0 0.0 44.52 42.0 \n", - "2 0.0 20.0 1.20 0.0 0.0 0.0 0.0 0.0 21.20 20.0 \n", - "3 0.0 40.0 2.40 0.0 0.0 0.0 0.0 0.0 42.40 40.0 \n", - "4 0.0 5.5 0.33 0.0 0.0 0.0 0.0 0.0 5.83 5.5 " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true, - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": { - "hidden": true - }, - "report_default": { - "hidden": true - } - } - } - } - }, - "outputs": [], - "source": [ - "#drop rows that have an NaN\n", - "df=df.dropna()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": { - "col": 0, - "height": 10, - "hidden": true, - "row": 20, - "width": 9 - }, - "report_default": { - "hidden": true - } - } - } - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Date/TimeStore_IDInv #Unnamed: 5Cashier IDPMTotal CostTotal PriceTax1Tax2Tax3Tax4Tax5Tax6GTotalGross
111/25/2017 11:58: AM1001.0128.0101.0100101.0CC0.042.02.520.00.00.00.00.044.5242.0
211/25/2017 12:08: PM1001.0129.0101.0100101.0CC0.020.01.200.00.00.00.00.021.2020.0
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511/25/2017 12:49: PM1001.0132.0101.0100101.0CC0.06.50.390.00.00.00.00.06.896.5
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" - ], - "text/plain": [ - " Date/Time Store_ID Inv # Unnamed: 5 Cashier ID PM \\\n", - "1 11/25/2017 11:58: AM 1001.0 128.0 101.0 100101.0 CC \n", - "2 11/25/2017 12:08: PM 1001.0 129.0 101.0 100101.0 CC \n", - "3 11/25/2017 12:09: PM 1001.0 130.0 101.0 100101.0 CA \n", - "4 11/25/2017 12:23: PM 1001.0 131.0 101.0 100101.0 CA \n", - "5 11/25/2017 12:49: PM 1001.0 132.0 101.0 100101.0 CC \n", - "\n", - " Total Cost Total Price Tax1 Tax2 Tax3 Tax4 Tax5 Tax6 GTotal Gross \n", - "1 0.0 42.0 2.52 0.0 0.0 0.0 0.0 0.0 44.52 42.0 \n", - "2 0.0 20.0 1.20 0.0 0.0 0.0 0.0 0.0 21.20 20.0 \n", - "3 0.0 40.0 2.40 0.0 0.0 0.0 0.0 0.0 42.40 40.0 \n", - "4 0.0 5.5 0.33 0.0 0.0 0.0 0.0 0.0 5.83 5.5 \n", - "5 0.0 6.5 0.39 0.0 0.0 0.0 0.0 0.0 6.89 6.5 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": { - "col": 0, - "height": 11, - "hidden": true, - "row": 18, - "width": 12 - }, - "report_default": { - "hidden": true - } - } - } - } - }, - "source": [ - "Now we have a relatively clean dataset where each of the columns represent:
\n", - " - __Date/Time__ : The date and time of the transaction\n", - " - __Store_ID__ : The store ID number\n", - " - __Inv #__ : Invoice number\n", - " - __Unnamed: 5__ : Another store ID\n", - " - __Cashier ID__ : Cashier ID number\n", - " - __PM__ : Payment method (CC = credit card, CA = cash)\n", - " - __Total Cost__ : Total cost to store\n", - " - __Total Price__ : Total price of purchase\n", - " - __Tax1__ : MD state tax\n", - " - __Tax2-Tax6__ : unnecessary columns\n", - " - __GTotal__ : Total price + MD State tax\n", - " - __Gross__ : Gross earnings" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true, - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": { - "hidden": true - }, - "report_default": { - "hidden": true - } - } - } - } - }, - "outputs": [], - "source": [ - "#remove Unnamed: 5 and Tax2-Tax6 unneccesary columns\n", - "tax_col = [3, 9, 10, 11, 12, 13]\n", - "df= df.drop(df.columns[[tax_col]], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": { - "col": 0, - "height": 10, - "hidden": true, - "row": 31, - "width": 6 - }, - "report_default": { - "hidden": true - } - } - } - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Date/TimeStore_IDInv #Cashier IDPMTotal CostTotal PriceTax1GTotalGross
111/25/2017 11:58: AM1001.0128.0100101.0CC0.042.02.5244.5242.0
211/25/2017 12:08: PM1001.0129.0100101.0CC0.020.01.2021.2020.0
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511/25/2017 12:49: PM1001.0132.0100101.0CC0.06.50.396.896.5
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" - ], - "text/plain": [ - " Date/Time Store_ID Inv # Cashier ID PM Total Cost \\\n", - "1 11/25/2017 11:58: AM 1001.0 128.0 100101.0 CC 0.0 \n", - "2 11/25/2017 12:08: PM 1001.0 129.0 100101.0 CC 0.0 \n", - "3 11/25/2017 12:09: PM 1001.0 130.0 100101.0 CA 0.0 \n", - "4 11/25/2017 12:23: PM 1001.0 131.0 100101.0 CA 0.0 \n", - "5 11/25/2017 12:49: PM 1001.0 132.0 100101.0 CC 0.0 \n", - "\n", - " Total Price Tax1 GTotal Gross \n", - "1 42.0 2.52 44.52 42.0 \n", - "2 20.0 1.20 21.20 20.0 \n", - "3 40.0 2.40 42.40 40.0 \n", - "4 5.5 0.33 5.83 5.5 \n", - "5 6.5 0.39 6.89 6.5 " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": { - "col": 0, - "height": 2, - "hidden": false, - "row": 12, - "width": 12 - }, - "report_default": { - "hidden": false - } - } - } - } - }, - "source": [ - "# Made in Baltimore Questions:" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": { - "col": 0, - "height": 2, - "hidden": false, - "row": 14, - "width": 12 - }, - "report_default": { - "hidden": false - } - } - } - } - }, - "source": [ - "### What days did the shop have the strongest sales?" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": { - "col": 6, - "height": 9, - "hidden": true, - "row": 31, - "width": 4 - }, - "report_default": { - "hidden": true - } - } - } - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 1055 entries, 1 to 1068\n", - "Data columns (total 10 columns):\n", - " Date/Time 1055 non-null object\n", - "Store_ID 1055 non-null float64\n", - "Inv # 1055 non-null float64\n", - "Cashier ID 1055 non-null float64\n", - "PM 1055 non-null object\n", - "Total Cost 1055 non-null float64\n", - "Total Price 1055 non-null float64\n", - "Tax1 1055 non-null float64\n", - "GTotal 1055 non-null float64\n", - "Gross 1055 non-null float64\n", - "dtypes: float64(8), object(2)\n", - "memory usage: 90.7+ KB\n" - ] - } - ], - "source": [ - "#look at the data types in our df\n", - "df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": { - "col": 6, - "height": 7, - "hidden": true, - "row": 40, - "width": 4 - }, - "report_default": { - "hidden": true - } - } - } - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[' Date/Time',\n", - " 'Store_ID',\n", - " 'Inv # ',\n", - " 'Cashier ID',\n", - " 'PM',\n", - " 'Total Cost',\n", - " 'Total Price',\n", - " 'Tax1',\n", - " 'GTotal',\n", - " 'Gross']" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns.tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": { - "col": 0, - "height": 12, - "hidden": true, - "row": 41, - "width": 4 - }, - "report_default": { - "hidden": true - } - } - } - }, - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['11/25/2017 11:58: AM',\n", - " '11/25/2017 12:08: PM',\n", - " '11/25/2017 12:09: PM',\n", - " '11/25/2017 12:23: PM',\n", - " '11/25/2017 12:49: PM',\n", - " '11/25/2017 12:56: PM',\n", - " '11/25/2017 1:45: PM',\n", - " '11/25/2017 1:59: PM',\n", - " '11/25/2017 2:00: PM',\n", - " '11/25/2017 2:25: PM',\n", - " '11/25/2017 2:32: PM',\n", - " '11/25/2017 2:36: PM',\n", - " '11/25/2017 2:37: PM',\n", - " '11/25/2017 2:39: PM',\n", - " '11/25/2017 2:41: PM',\n", - " '11/25/2017 2:42: PM',\n", - " '11/25/2017 2:44: PM',\n", - " '11/25/2017 2:47: PM',\n", - " '11/25/2017 2:48: PM',\n", - 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111/25/2017 11:58: AM1001.0128.0100101.0CC0.042.02.5244.5242.011/25/201711:58: AM2017-11-25 11:58:00115Saturday2017-11-25861.012939.234350.93
211/25/2017 12:08: PM1001.0129.0100101.01.2021.2020.011/25/201712:08: PM2017-11-25 12:08:00125Saturday2017-11-25861.012939.236478.18
311/25/2017 12:09: PM1001.0130.0100101.02.4042.4040.011/25/201712:09: PM2017-11-25 12:09:00125Saturday2017-11-25861.012939.236478.18
411/25/2017 12:23: PM1001.0131.0100101.00.335.835.511/25/201712:23: PM2017-11-25 12:23:00125Saturday2017-11-25861.012939.236478.18
511/25/2017 12:49: PM1001.0132.0100101.00.396.896.511/25/201712:49: PM2017-11-25 12:49:00125Saturday2017-11-25861.012939.236478.18
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" ], "text/plain": [ - " Date/Time Store_ID Inv # Cashier ID PM Total Cost \\\n", - "1 11/25/2017 11:58: AM 1001.0 128.0 100101.0 CC 0.0 \n", - "2 11/25/2017 12:08: PM 1001.0 129.0 100101.0 CC 0.0 \n", - "3 11/25/2017 12:09: PM 1001.0 130.0 100101.0 CA 0.0 \n", - "4 11/25/2017 12:23: PM 1001.0 131.0 100101.0 CA 0.0 \n", - "5 11/25/2017 12:49: PM 1001.0 132.0 100101.0 CC 0.0 \n", + " Store_ID Inv # Cashier ID PM Total Cost Total Price Tax1 GTotal \\\n", + "1 1001.0 128.0 100101.0 CC 0.0 42.0 2.52 44.52 \n", + "2 1001.0 129.0 100101.0 CC 0.0 20.0 1.20 21.20 \n", + "3 1001.0 130.0 100101.0 CA 0.0 40.0 2.40 42.40 \n", + "4 1001.0 131.0 100101.0 CA 0.0 5.5 0.33 5.83 \n", + "5 1001.0 132.0 100101.0 CC 0.0 6.5 0.39 6.89 \n", "\n", - " Total Price Tax1 GTotal Gross Date Time \n", - "1 42.0 2.52 44.52 42.0 11/25/2017 11:58: AM \n", - "2 20.0 1.20 21.20 20.0 11/25/2017 12:08: PM \n", - "3 40.0 2.40 42.40 40.0 11/25/2017 12:09: PM \n", - "4 5.5 0.33 5.83 5.5 11/25/2017 12:23: PM \n", - "5 6.5 0.39 6.89 6.5 11/25/2017 12:49: PM " + " Gross Date hour day_number day_name shop_date \\\n", + "1 42.0 2017-11-25 11:58:00 11 5 Saturday 2017-11-25 \n", + "2 20.0 2017-11-25 12:08:00 12 5 Saturday 2017-11-25 \n", + "3 40.0 2017-11-25 12:09:00 12 5 Saturday 2017-11-25 \n", + "4 5.5 2017-11-25 12:23:00 12 5 Saturday 2017-11-25 \n", + "5 6.5 2017-11-25 12:49:00 12 5 Saturday 2017-11-25 \n", + "\n", + " shop_date_total day_total hour_total \n", + "1 861.0 12939.23 4350.93 \n", + "2 861.0 12939.23 6478.18 \n", + "3 861.0 12939.23 6478.18 \n", + "4 861.0 12939.23 6478.18 \n", + "5 861.0 12939.23 6478.18 " ] }, - "execution_count": 19, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -3722,16 +366,43 @@ ] }, { - "cell_type": "code", - "execution_count": 20, + "cell_type": "markdown", "metadata": { - "collapsed": true, "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { + "col": 0, + "height": 4, + "hidden": true, + "row": 14, + "width": 4 + }, + "report_default": { "hidden": true + } + } + } + } + }, + "source": [ + "# Clean the Data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": 0, + "height": 4, + "hidden": true, + "row": 20, + "width": 12 }, "report_default": { "hidden": true @@ -3740,15 +411,13 @@ } } }, - "outputs": [], "source": [ - "#split the Time column to get rid of the second colon\n", - "df['hour'],df['min'], df['AM/PM']= df['Time'].str.split (':', 2).str" + "I edited the original excel sheet to reformat the data because there were daily totals inline with the transaction data. Although fairly straightforward, there were still some empty columns and rows once I imported into this notebook, so we will need to clean the dataset:" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 9, "metadata": { "collapsed": true, "extensions": { @@ -3767,13 +436,14 @@ }, "outputs": [], "source": [ - "#create new column with time reformatted\n", - "df['Date']= df['Date'] + df['hour'] + \":\" + df['min'] + df['AM/PM']" + "#drop columns that are all NaN\n", + "col = [2,3,6,7,12,19, 22] #column numbers are found from the column header \"Unnamed: #\"\n", + "df= df.drop(df.columns[[col]], axis=1)" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 11, "metadata": { "collapsed": true, "extensions": { @@ -3792,13 +462,12 @@ }, "outputs": [], "source": [ - "#change date column into datetime data type\n", - "df['Date']=pd.to_datetime(df['Date'])" + "#drop rows that have an NaN\n", + "df=df.dropna()" ] }, { - "cell_type": "code", - "execution_count": 23, + "cell_type": "markdown", "metadata": { "extensions": { "jupyter_dashboards": { @@ -3806,52 +475,37 @@ "views": { "grid_default": { "col": 0, - "height": 9, + "height": 11, "hidden": true, - "row": 41, - "width": 4 + "row": 18, + "width": 12 }, "report_default": { "hidden": true } } } - }, - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[' Date/Time',\n", - " 'Store_ID',\n", - " 'Inv # ',\n", - " 'Cashier ID',\n", - " 'PM',\n", - " 'Total Cost',\n", - " 'Total Price',\n", - " 'Tax1',\n", - " 'GTotal',\n", - " 'Gross',\n", - " 'Date',\n", - " 'Time',\n", - " 'hour',\n", - " 'min',\n", - " 'AM/PM']" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" } - ], + }, "source": [ - "df.columns.tolist()" + "Now we have a relatively clean dataset where each of the columns represent:
\n", + " - __Date/Time__ : The date and time of the transaction\n", + " - __Store_ID__ : The store ID number\n", + " - __Inv #__ : Invoice number\n", + " - __Unnamed: 5__ : Another store ID\n", + " - __Cashier ID__ : Cashier ID number\n", + " - __PM__ : Payment method (CC = credit card, CA = cash)\n", + " - __Total Cost__ : Total cost to store\n", + " - __Total Price__ : Total price of purchase\n", + " - __Tax1__ : MD state tax\n", + " - __Tax2-Tax6__ : unnecessary columns\n", + " - __GTotal__ : Total price + MD State tax\n", + " - __Gross__ : Gross earnings" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 13, "metadata": { "collapsed": true, "extensions": { @@ -3870,78 +524,64 @@ }, "outputs": [], "source": [ - "#keep only necessary columns\n", - "MIB_col = [\n", - " 'Store_ID',\n", - " 'Inv # ',\n", - " 'Cashier ID',\n", - " 'PM',\n", - " 'Total Cost',\n", - " 'Total Price',\n", - " 'Tax1',\n", - " 'GTotal',\n", - " 'Gross',\n", - " 'Date']\n", - "\n", - "df = df[MIB_col]" + "#remove Unnamed: 5 and Tax2-Tax6 unneccesary columns\n", + "tax_col = [3, 9, 10, 11, 12, 13]\n", + "df= df.drop(df.columns[[tax_col]], axis=1)" ] }, { - "cell_type": "code", - "execution_count": 25, + "cell_type": "markdown", "metadata": { - "collapsed": true, "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { - "hidden": true + "col": 0, + "height": 2, + "hidden": false, + "row": 12, + "width": 12 }, "report_default": { - "hidden": true + "hidden": false } } } } }, - "outputs": [], "source": [ - "#make columns that have hours of day and days of the week listed in numbers and names\n", - "\n", - "df['hour']=df.Date.apply(lambda x: x.hour)\n", - "df['day_number']=df.Date.apply(lambda x: x.dayofweek)\n", - "df['day_name']=df.Date.dt.weekday_name" + "# Made in Baltimore Questions:" ] }, { - "cell_type": "code", - "execution_count": 26, + "cell_type": "markdown", "metadata": { - "collapsed": true, "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { - "hidden": true + "col": 0, + "height": 2, + "hidden": false, + "row": 14, + "width": 12 }, "report_default": { - "hidden": true + "hidden": false } } } } }, - "outputs": [], "source": [ - "#make new column that only lists date so we can groupby shop date\n", - "df['shop_date']= df['Date'].dt.date" + "### What days did the shop have the strongest sales?" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 18, "metadata": { "collapsed": true, "extensions": { @@ -3960,13 +600,14 @@ }, "outputs": [], "source": [ - "#sum Gross earnings per shop date\n", - "df['shop_date_total']= df.groupby('shop_date')['Gross'].transform('sum')" + "#split Date/Time column into date and time columns\n", + "\n", + "df['Date'], df['Time'] = df[' Date/Time'].str.split(' ',1).str" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 20, "metadata": { "collapsed": true, "extensions": { @@ -3985,470 +626,198 @@ }, "outputs": [], "source": [ - "#filter and make new dataframe so that we only have one row per date\n", - "df_day_sum=df.drop_duplicates(subset='shop_date')" + "#split the Time column to get rid of the second colon\n", + "df['hour'],df['min'], df['AM/PM']= df['Time'].str.split (':', 2).str" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 21, "metadata": { + "collapsed": true, "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { - "col": 0, - "height": 12, - "hidden": true, - "row": 45, - "width": 10 + "hidden": true }, "report_default": { "hidden": true } } } - }, - "scrolled": true + } }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Store_IDInv #Cashier IDPMTotal CostTotal PriceTax1GTotalGrossDatehourday_numberday_nameshop_dateshop_date_total
11001.0128.0100101.0CC0.042.002.5244.5242.002017-11-25 11:58:00115Saturday2017-11-25861.00
321001.0159.0100101.0CC0.016.000.9616.9616.002017-11-26 11:38:00116Sunday2017-11-261038.94
581001.0184.0100101.0CC0.01.200.071.271.202017-12-01 11:29:00114Friday2017-12-012615.90
1351001.0260.0100101.0CA0.020.001.2021.2020.002017-12-02 11:22:00115Saturday2017-12-022824.92
2171001.0342.0100101.0CC0.015.000.9015.9015.002017-12-03 11:16:00116Sunday2017-12-033048.38
2901001.0415.0100101.0CH0.0161.509.69171.19161.502017-12-08 11:24:00114Friday2017-12-081864.14
3361001.0460.0100101.0CC120.0240.0014.40254.40120.002017-12-09 11:33:00115Saturday2017-12-092337.31
4091001.0532.0100101.0CC0.013.490.8114.3013.492017-12-10 11:12:00116Sunday2017-12-104231.02
5051001.0627.0100101.0CC34.0198.0011.88209.88164.002017-12-15 11:35:00114Friday2017-12-151993.83
5531001.0674.0100101.0CC0.044.502.6747.1744.502017-12-16 11:24:00115Saturday2017-12-164348.14
6821001.0802.0100101.0CC0.058.003.4861.4858.002017-12-17 11:29:00116Sunday2017-12-173262.01
7581001.0877.0100101.0CA0.040.002.4042.4040.002017-12-18 11:11:00110Monday2017-12-18650.08
7831001.0902.0100101.0CC25.025.001.5026.500.002017-12-19 11:37:00111Tuesday2017-12-191206.03
8131001.0931.0100101.0CA14.815.000.9015.900.202017-12-20 11:50:00112Wednesday2017-12-201297.06
8571001.0974.0100101.0CC0.0110.006.60116.60110.002017-12-21 11:18:00113Thursday2017-12-211496.18
9001001.01016.0100101.0CC40.088.005.2893.2848.002017-12-22 11:10:00114Friday2017-12-222094.69
9711001.01086.0100101.0CA10.010.000.6010.600.002017-12-23 11:13:00115Saturday2017-12-232567.86
10371001.01152.0100101.0CC0.010.000.6010.6010.002017-12-24 11:44:00116Sunday2017-12-24948.33
\n", - "
" - ], - "text/plain": [ - " Store_ID Inv # Cashier ID PM Total Cost Total Price Tax1 \\\n", - "1 1001.0 128.0 100101.0 CC 0.0 42.00 2.52 \n", - "32 1001.0 159.0 100101.0 CC 0.0 16.00 0.96 \n", - "58 1001.0 184.0 100101.0 CC 0.0 1.20 0.07 \n", - "135 1001.0 260.0 100101.0 CA 0.0 20.00 1.20 \n", - "217 1001.0 342.0 100101.0 CC 0.0 15.00 0.90 \n", - "290 1001.0 415.0 100101.0 CH 0.0 161.50 9.69 \n", - "336 1001.0 460.0 100101.0 CC 120.0 240.00 14.40 \n", - "409 1001.0 532.0 100101.0 CC 0.0 13.49 0.81 \n", - "505 1001.0 627.0 100101.0 CC 34.0 198.00 11.88 \n", - "553 1001.0 674.0 100101.0 CC 0.0 44.50 2.67 \n", - "682 1001.0 802.0 100101.0 CC 0.0 58.00 3.48 \n", - "758 1001.0 877.0 100101.0 CA 0.0 40.00 2.40 \n", - "783 1001.0 902.0 100101.0 CC 25.0 25.00 1.50 \n", - "813 1001.0 931.0 100101.0 CA 14.8 15.00 0.90 \n", - "857 1001.0 974.0 100101.0 CC 0.0 110.00 6.60 \n", - "900 1001.0 1016.0 100101.0 CC 40.0 88.00 5.28 \n", - "971 1001.0 1086.0 100101.0 CA 10.0 10.00 0.60 \n", - "1037 1001.0 1152.0 100101.0 CC 0.0 10.00 0.60 \n", - "\n", - " GTotal Gross Date hour day_number day_name \\\n", - "1 44.52 42.00 2017-11-25 11:58:00 11 5 Saturday \n", - "32 16.96 16.00 2017-11-26 11:38:00 11 6 Sunday \n", - "58 1.27 1.20 2017-12-01 11:29:00 11 4 Friday \n", - "135 21.20 20.00 2017-12-02 11:22:00 11 5 Saturday \n", - "217 15.90 15.00 2017-12-03 11:16:00 11 6 Sunday \n", - "290 171.19 161.50 2017-12-08 11:24:00 11 4 Friday \n", - "336 254.40 120.00 2017-12-09 11:33:00 11 5 Saturday \n", - "409 14.30 13.49 2017-12-10 11:12:00 11 6 Sunday \n", - "505 209.88 164.00 2017-12-15 11:35:00 11 4 Friday \n", - "553 47.17 44.50 2017-12-16 11:24:00 11 5 Saturday \n", - "682 61.48 58.00 2017-12-17 11:29:00 11 6 Sunday \n", - "758 42.40 40.00 2017-12-18 11:11:00 11 0 Monday \n", - "783 26.50 0.00 2017-12-19 11:37:00 11 1 Tuesday \n", - "813 15.90 0.20 2017-12-20 11:50:00 11 2 Wednesday \n", - "857 116.60 110.00 2017-12-21 11:18:00 11 3 Thursday \n", - "900 93.28 48.00 2017-12-22 11:10:00 11 4 Friday \n", - "971 10.60 0.00 2017-12-23 11:13:00 11 5 Saturday \n", - "1037 10.60 10.00 2017-12-24 11:44:00 11 6 Sunday \n", - "\n", - " shop_date shop_date_total \n", - "1 2017-11-25 861.00 \n", - "32 2017-11-26 1038.94 \n", - "58 2017-12-01 2615.90 \n", - "135 2017-12-02 2824.92 \n", - "217 2017-12-03 3048.38 \n", - "290 2017-12-08 1864.14 \n", - "336 2017-12-09 2337.31 \n", - "409 2017-12-10 4231.02 \n", - "505 2017-12-15 1993.83 \n", - "553 2017-12-16 4348.14 \n", - "682 2017-12-17 3262.01 \n", - "758 2017-12-18 650.08 \n", - "783 2017-12-19 1206.03 \n", - "813 2017-12-20 1297.06 \n", - "857 2017-12-21 1496.18 \n", - "900 2017-12-22 2094.69 \n", - "971 2017-12-23 2567.86 \n", - "1037 2017-12-24 948.33 " - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" + "outputs": [], + "source": [ + "#create new column with time reformatted\n", + "df['Date']= df['Date'] + df['hour'] + \":\" + df['min'] + df['AM/PM']" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } } - ], + }, + "outputs": [], + "source": [ + "#change date column into datetime data type\n", + "df['Date']=pd.to_datetime(df['Date'])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } + }, + "outputs": [], + "source": [ + "#keep only necessary columns\n", + "MIB_col = [\n", + " 'Store_ID',\n", + " 'Inv # ',\n", + " 'Cashier ID',\n", + " 'PM',\n", + " 'Total Cost',\n", + " 'Total Price',\n", + " 'Tax1',\n", + " 'GTotal',\n", + " 'Gross',\n", + " 'Date']\n", + "\n", + "df = df[MIB_col]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } + }, + "outputs": [], + "source": [ + "#make columns that have hours of day and days of the week listed in numbers and names\n", + "\n", + "df['hour']=df.Date.apply(lambda x: x.hour)\n", + "df['day_number']=df.Date.apply(lambda x: x.dayofweek)\n", + "df['day_name']=df.Date.dt.weekday_name" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } + }, + "outputs": [], + "source": [ + "#make new column that only lists date so we can groupby shop date\n", + "df['shop_date']= df['Date'].dt.date" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } + }, + "outputs": [], + "source": [ + "#sum Gross earnings per shop date\n", + "df['shop_date_total']= df.groupby('shop_date')['Gross'].transform('sum')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } + }, + "outputs": [], "source": [ - "df_day_sum" + "#filter and make new dataframe so that we only have one row per date\n", + "df_day_sum=df.drop_duplicates(subset='shop_date')" ] }, { @@ -4738,28 +1107,6 @@ "The lowest earnings day was Monday, December 18, which could have been because this was the first weekday that the Made in Baltimore Holiday shop was open. The Made in Baltimore instagram posted 2 instagram posts on December 19, compared to their average 1 post every 2 days, perhaps as a reaction to the lowest sales earnings on Monday, December 18. The Made in Baltimore instagram posted at least once per day from December 19-22, which could have helped increase sales during the last week of the Holiday Shop.\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": { - "hidden": true - }, - "report_default": { - "hidden": true - } - } - } - } - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": { @@ -10394,78 +6741,6 @@ "This shows that the holiday popup shop earned the most in sales during the hours of 12 PM and 1 PM. Sales show a relatively even distribution between 11 AM (opening) and 4 PM, although sales at 5 PM (\\$3,077.73) and 6 PM (\\$2,608) are not insignificant. Sales after 7 PM decline sharply, which might suggest that sales might be more successful if there are additional shops or locations open from 11 AM-4 PM instead of having the shop open for later hours. " ] }, - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": { - "col": 0, - "height": 4, - "hidden": true, - "row": 33, - "width": 4 - }, - "report_default": { - "hidden": true - } - } - } - } - }, - "source": [ - "### Can we find any correlations between time or day and sales?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": { - "hidden": true - }, - "report_default": { - "hidden": true - } - } - } - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": { - "col": 0, - "height": 4, - "hidden": true, - "row": 41, - "width": 4 - }, - "report_default": { - "hidden": true - } - } - } - } - }, - "source": [ - "### What additional data would help us determine marketing outreach and increase sales for next year's Holiday Shop?" - ] - }, { "cell_type": "code", "execution_count": null,