diff --git a/March2018_Meetup/Made in Baltimore - Popup Shop Analysis.ipynb b/March2018_Meetup/Made in Baltimore - Popup Shop Analysis.ipynb index dfcecb1..f3be09f 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": 37, + "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": { @@ -79,181 +209,154 @@ " \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", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", " 1\n", - " 11/25/2017 11:58: AM\n", " 1001.0\n", - " NaN\n", - " NaN\n", " 128.0\n", - " 101.0\n", - " NaN\n", - " NaN\n", " 100101.0\n", " CC\n", - " ...\n", - " 2.52\n", - " 0.0\n", " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " NaN\n", + " 42.0\n", + " 2.52\n", " 44.52\n", " 42.0\n", - " NaN\n", + " 2017-11-25 11:58:00\n", + " 11\n", + " 5\n", + " Saturday\n", + " 2017-11-25\n", + " 861.0\n", + " 12939.23\n", + " 4350.93\n", " \n", " \n", " 2\n", - " 11/25/2017 12:08: PM\n", " 1001.0\n", - " NaN\n", - " NaN\n", " 129.0\n", - " 101.0\n", - " NaN\n", - " NaN\n", " 100101.0\n", " CC\n", - " ...\n", - " 1.20\n", - " 0.0\n", " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " NaN\n", + " 20.0\n", + " 1.20\n", " 21.20\n", " 20.0\n", - " NaN\n", + " 2017-11-25 12:08:00\n", + " 12\n", + " 5\n", + " Saturday\n", + " 2017-11-25\n", + " 861.0\n", + " 12939.23\n", + " 6478.18\n", " \n", " \n", " 3\n", - " 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", - " 2.40\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", " 0.0\n", - " 0.0\n", - " NaN\n", + " 40.0\n", + " 2.40\n", " 42.40\n", " 40.0\n", - " NaN\n", + " 2017-11-25 12:09:00\n", + " 12\n", + " 5\n", + " Saturday\n", + " 2017-11-25\n", + " 861.0\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", - " 0.33\n", - " 0.0\n", " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " NaN\n", + " 5.5\n", + " 0.33\n", " 5.83\n", " 5.5\n", - " NaN\n", + " 2017-11-25 12:23:00\n", + " 12\n", + " 5\n", + " Saturday\n", + " 2017-11-25\n", + " 861.0\n", + " 12939.23\n", + " 6478.18\n", + " \n", + " \n", + " 5\n", + " 1001.0\n", + " 132.0\n", + " 100101.0\n", + " CC\n", + " 0.0\n", + " 6.5\n", + " 0.39\n", + " 6.89\n", + " 6.5\n", + " 2017-11-25 12:49:00\n", + " 12\n", + " 5\n", + " Saturday\n", + " 2017-11-25\n", + " 861.0\n", + " 12939.23\n", + " 6478.18\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", + " 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", - " 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", + " 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", - "[5 rows x 23 columns]" + " 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": 4, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -264,22 +367,73 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "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": {}, + "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": 5, - "metadata": {}, + "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", @@ -289,3975 +443,4087 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": { - "scrolled": true + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": 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: PM1001.0131.0101.0100101.0CA0.005.500.330.00.00.00.00.05.835.50
511/25/2017 12:49: PM1001.0132.0101.0100101.0CC0.006.500.390.00.00.00.00.06.896.50
611/25/2017 12:56: PM1001.0133.0101.0100101.0CC0.0030.501.830.00.00.00.00.032.3330.50
711/25/2017 1:45: PM1001.0134.0101.0100101.0CA0.0035.002.100.00.00.00.00.037.1035.00
811/25/2017 1:59: PM1001.0135.0101.0100101.0CC0.0052.503.150.00.00.00.00.055.6552.50
911/25/2017 2:00: PM1001.0136.0101.0100101.0CA0.006.500.390.00.00.00.00.06.896.50
1011/25/2017 2:25: PM1001.0137.0101.0100101.0CC0.0015.000.900.00.00.00.00.015.9015.00
1111/25/2017 2:32: PM1001.0138.0101.0100101.0CC0.0013.000.780.00.00.00.00.013.7813.00
1211/25/2017 2:36: PM1001.0139.0101.0100101.0CC0.0028.001.680.00.00.00.00.029.6828.00
1311/25/2017 2:37: PM1001.0140.0101.0100101.0CA0.0037.002.220.00.00.00.00.039.2237.00
1411/25/2017 2:39: PM1001.0141.0101.0100101.0CA0.005.500.330.00.00.00.00.05.835.50
1511/25/2017 2:41: PM1001.0142.0101.0100101.0CA0.0035.002.100.00.00.00.00.037.1035.00
1611/25/2017 2:42: PM1001.0143.0101.0100101.0CA0.0025.001.500.00.00.00.00.026.5025.00
1711/25/2017 2:44: PM1001.0144.0101.0100101.0CC0.0011.000.660.00.00.00.00.011.6611.00
1811/25/2017 2:47: PM1001.0145.0101.0100101.0CC0.0046.502.790.00.00.00.00.049.2946.50
1911/25/2017 2:48: PM1001.0146.0101.0100101.0CA0.0015.000.900.00.00.00.00.015.9015.00
2011/25/2017 3:09: PM1001.0147.0101.0100101.0CA0.0030.001.800.00.00.00.00.031.8030.00
2111/25/2017 3:35: PM1001.0148.0101.0100101.0CC0.0063.003.780.00.00.00.00.066.7863.00
2211/25/2017 3:41: PM1001.0149.0101.0100101.0CC0.0036.002.160.00.00.00.00.038.1636.00
2311/25/2017 3:45: PM1001.0150.0101.0100101.0CC0.0042.002.520.00.00.00.00.044.5242.00
2411/25/2017 3:47: PM1001.0151.0101.0100101.0CC0.0030.501.830.00.00.00.00.032.3330.50
2511/25/2017 3:54: PM1001.0152.0101.0100101.0CA0.0012.000.720.00.00.00.00.012.7212.00
2611/25/2017 3:56: PM1001.0153.0101.0100101.0CC0.0016.500.990.00.00.00.00.017.4916.50
2711/25/2017 4:04: PM1001.0154.0101.0100101.0CC0.0030.001.800.00.00.00.00.031.8030.00
2811/25/2017 4:51: PM1001.0155.0101.0100101.0CC0.0037.002.220.00.00.00.00.039.2237.00
2911/25/2017 5:03: PM1001.0156.0101.0100101.0CC0.006.500.390.00.00.00.00.06.896.50
...................................................
104312/24/2017 12:10: PM1001.01158.0101.0100101.0CA0.0026.001.560.00.00.00.00.027.5626.00
104412/24/2017 12:15: PM1001.01159.0101.0100101.0CA20.7933.502.010.00.00.00.00.035.5112.71
104512/24/2017 12:18: PM1001.01160.0101.0100101.0CC23.2839.982.400.00.00.00.00.042.3816.70
104612/24/2017 12:20: PM1001.01161.0101.0100101.0CC12.0022.001.320.00.00.00.00.023.3210.00
104712/24/2017 12:39: PM1001.01162.0101.0100101.0CA14.8815.000.900.00.00.00.00.015.900.12
104812/24/2017 12:53: PM1001.01163.0101.0100101.0CC20.00120.007.200.00.00.00.00.0127.20100.00
104912/24/2017 12:55: PM1001.01164.0101.0100101.0CC5.1535.502.130.00.00.00.00.037.6330.35
105012/24/2017 1:04: PM1001.01165.0101.0100101.0CC33.5738.002.280.00.00.00.00.040.284.43
105112/24/2017 1:10: PM1001.01166.0101.0100101.0CC12.0025.991.560.00.00.00.00.027.5513.99
105212/24/2017 1:11: PM1001.01167.0101.0100101.0CC11.6735.002.100.00.00.00.00.037.1023.33
105312/24/2017 1:18: PM1001.01168.0101.0100101.0CC0.0060.003.600.00.00.00.00.063.6060.00
105412/24/2017 1:24: PM1001.01169.0101.0100101.0CC65.71110.006.600.00.00.00.00.0116.6044.29
105512/24/2017 1:35: PM1001.01170.0101.0100101.0CC25.0040.002.400.00.00.00.00.042.4015.00
105612/24/2017 1:49: PM1001.01171.0101.0100101.0CC43.67183.0010.980.00.00.00.00.0193.98139.33
105712/24/2017 1:50: PM1001.01172.0101.0100101.0CA5.0020.001.200.00.00.00.00.021.2015.00
105812/24/2017 1:58: PM1001.01173.0101.0100101.0CA0.0010.000.600.00.00.00.00.010.6010.00
105912/24/2017 2:00: PM1001.01174.0101.0100101.0CC1.8213.000.780.00.00.00.00.013.7811.18
106012/24/2017 2:11: PM1001.01175.0101.0100101.0CC37.5049.002.940.00.00.00.00.051.9411.50
106112/24/2017 2:35: PM1001.01176.0101.0100101.0CC39.73116.506.990.00.00.00.00.0123.4976.77
106212/24/2017 2:50: PM1001.01177.0101.0100101.0CC5.505.500.330.00.00.00.00.05.830.00
106312/24/2017 3:12: PM1001.01178.0101.0100101.0CA0.005.000.300.00.00.00.00.05.305.00
106412/24/2017 3:21: PM1001.01179.0101.0100101.0CC0.0095.005.700.00.00.00.00.0100.7095.00
106512/24/2017 4:08: PM1001.01180.0101.0100101.0CA1.9318.001.080.00.00.00.00.019.0816.07
106612/24/2017 4:17: PM1001.01181.0101.0100101.0CC0.0030.001.800.00.00.00.00.031.8030.00
106712/24/2017 5:03: PM1001.01182.0101.0100101.0CA0.0015.000.900.00.00.00.00.015.9015.00
106812/24/2017 5:18: PM1001.01183.0101.0100101.0CC117.49127.007.620.00.00.00.00.0134.629.51
1069NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1070NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1071NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1072NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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"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", - "1049 5.15 35.50 2.13 0.0 0.0 0.0 0.0 0.0 37.63 \n", - "1050 33.57 38.00 2.28 0.0 0.0 0.0 0.0 0.0 40.28 \n", - "1051 12.00 25.99 1.56 0.0 0.0 0.0 0.0 0.0 27.55 \n", - "1052 11.67 35.00 2.10 0.0 0.0 0.0 0.0 0.0 37.10 \n", - "1053 0.00 60.00 3.60 0.0 0.0 0.0 0.0 0.0 63.60 \n", - "1054 65.71 110.00 6.60 0.0 0.0 0.0 0.0 0.0 116.60 \n", - "1055 25.00 40.00 2.40 0.0 0.0 0.0 0.0 0.0 42.40 \n", - "1056 43.67 183.00 10.98 0.0 0.0 0.0 0.0 0.0 193.98 \n", - 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"8 52.50 \n", - "9 6.50 \n", - "10 15.00 \n", - "11 13.00 \n", - "12 28.00 \n", - "13 37.00 \n", - "14 5.50 \n", - "15 35.00 \n", - "16 25.00 \n", - "17 11.00 \n", - "18 46.50 \n", - "19 15.00 \n", - "20 30.00 \n", - "21 63.00 \n", - "22 36.00 \n", - "23 42.00 \n", - "24 30.50 \n", - "25 12.00 \n", - "26 16.50 \n", - "27 30.00 \n", - "28 37.00 \n", - "29 6.50 \n", - "... ... \n", - "1043 26.00 \n", - "1044 12.71 \n", - "1045 16.70 \n", - "1046 10.00 \n", - "1047 0.12 \n", - "1048 100.00 \n", - "1049 30.35 \n", - "1050 4.43 \n", - "1051 13.99 \n", - "1052 23.33 \n", - "1053 60.00 \n", - "1054 44.29 \n", - "1055 15.00 \n", - "1056 139.33 \n", - "1057 15.00 \n", - "1058 10.00 \n", - "1059 11.18 \n", - "1060 11.50 \n", - "1061 76.77 \n", - "1062 0.00 \n", - "1063 5.00 \n", - "1064 95.00 \n", - "1065 16.07 \n", - "1066 30.00 \n", - "1067 15.00 \n", - "1068 9.51 \n", - "1069 NaN \n", - "1070 NaN \n", - "1071 NaN \n", - "1072 NaN \n", - "\n", - "[1073 rows x 16 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "#drop rows that have an NaN\n", - "df=df.dropna()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "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
311/25/2017 12:09: PM1001.0130.0101.0100101.0CA0.040.02.400.00.00.00.00.042.4040.0
411/25/2017 12:23: PM1001.0131.0101.0100101.0CA0.05.50.330.00.00.00.00.05.835.5
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": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "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": 9, - "metadata": {}, - "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": 10, - "metadata": {}, - "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
311/25/2017 12:09: PM1001.0130.0100101.0CA0.040.02.4042.4040.0
411/25/2017 12:23: PM1001.0131.0100101.0CA0.05.50.335.835.5
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": 10, - "metadata": {}, - "output_type": "execute_result" + "outputs": [], + "source": [ + "#drop rows that have an NaN\n", + "df=df.dropna()" + ] + }, + { + "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": [ - "df.head()" + "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": "markdown", - "metadata": {}, + "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": {}, + "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": 11, - "metadata": {}, - "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" - ] + "execution_count": 18, + "metadata": { + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } } - ], + }, + "outputs": [], "source": [ - "#look at the data types in our df\n", - "df.info()" + "#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": 12, - "metadata": {}, - "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": 12, - "metadata": {}, - "output_type": "execute_result" + "execution_count": 20, + "metadata": { + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } } - ], + }, + "outputs": [], "source": [ - "df.columns.tolist()" + "#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": 13, + "execution_count": 21, "metadata": { - "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", - 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Date/TimeStore_IDInv #Cashier IDPMTotal CostTotal PriceTax1GTotalGrossDateTime
111/25/2017 11:58: AM1001.0128.0100101.0CC0.042.02.5244.5242.011/25/201711:58: AM
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], + }, + "outputs": [], "source": [ - "df.head()" + "#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": 16, - "metadata": {}, + "execution_count": 25, + "metadata": { + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } + }, "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" + "#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": 17, - "metadata": {}, + "execution_count": 26, + "metadata": { + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } + }, "outputs": [], "source": [ - "#create new column with time reformatted\n", - "df['Date']= df['Date'] + df['hour'] + \":\" + df['min'] + df['AM/PM']" + "#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": 18, - "metadata": {}, + "execution_count": 27, + "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'])" + "#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": [ + "#filter and make new dataframe so that we only have one row per date\n", + "df_day_sum=df.drop_duplicates(subset='shop_date')" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 30, "metadata": { + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": 4, + "height": 4, + "hidden": true, + "row": 27, + "width": 4 + }, + "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']" + "text/html": [ + "" + ], + "text/vnd.plotly.v1+html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/vnd.plotly.v1+html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#make distribution visualizations in plotly\n", + "#import plotly\n", + "import plotly\n", + "from plotly.graph_objs import *\n", + "from plotly.offline import download_plotlyjs, init_notebook_mode, iplot\n", + "plotly.offline.init_notebook_mode()\n", + "import plotly.figure_factory as ff\n", + "\n", + "import cufflinks as cf\n", + "cf.go_offline()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": null, + "height": 18, + "hidden": false, + "row": 79, + "width": null + }, + "report_default": { + "hidden": false + } + } + } + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.6/site-packages/cufflinks/plotlytools.py:156: FutureWarning:\n", + "\n", + "pandas.tslib is deprecated and will be removed in a future version.\n", + "You can access Timestamp as pandas.Timestamp\n", + "\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "marker": { + "color": "rgba(255, 153, 51, 0.6)", + "line": { + "color": "rgba(255, 153, 51, 1.0)", + "width": 1 + } + }, + "name": "shop_date_total", + "orientation": "v", + "text": [ + "Saturday", + "Sunday", + "Friday", + "Saturday", + "Sunday", + "Friday", + "Saturday", + "Sunday", + "Friday", + "Saturday", + "Sunday", + "Monday", + "Tuesday", + "Wednesday", + "Thursday", + "Friday", + "Saturday", + "Sunday" + ], + "type": "bar", + "x": [ + "2017-11-25", + "2017-11-26", + "2017-12-01", + "2017-12-02", + "2017-12-03", + "2017-12-08", + "2017-12-09", + "2017-12-10", + "2017-12-15", + "2017-12-16", + "2017-12-17", + "2017-12-18", + "2017-12-19", + "2017-12-20", + "2017-12-21", + "2017-12-22", + "2017-12-23", + "2017-12-24" + ], + "y": [ + 861, + 1038.94, + 2615.8999999999996, + 2824.92, + 3048.3799999999997, + 1864.1399999999999, + 2337.31, + 4231.02, + 1993.83, + 4348.14, + 3262.0100000000007, + 650.0799999999999, + 1206.0300000000002, + 1297.0600000000002, + 1496.18, + 2094.6899999999996, + 2567.8599999999997, + 948.33 + ] + } + ], + "layout": { + "legend": { + "bgcolor": "#F5F6F9", + "font": { + "color": "#4D5663" + } + }, + "paper_bgcolor": "#F5F6F9", + "plot_bgcolor": "#F5F6F9", + "title": "Made in Baltimore Holiday Shop Earnings per Day", + "titlefont": { + "color": "#4D5663" + }, + "xaxis1": { + "gridcolor": "#E1E5ED", + "showgrid": true, + "tickfont": { + "color": "#4D5663" + }, + "title": "Popup Shop Date", + "titlefont": { + "color": "#4D5663" + }, + "zerolinecolor": "#E1E5ED" + }, + "yaxis1": { + "gridcolor": "#E1E5ED", + "showgrid": true, + "tickfont": { + "color": "#4D5663" + }, + "title": "Gross Earnings per Date", + "titlefont": { + "color": "#4D5663" + }, + "zerolinecolor": "#E1E5ED" + } + } + }, + "text/html": [ + "
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" ] }, - "execution_count": 19, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "df.columns.tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "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": 21, - "metadata": {}, - "outputs": [], - "source": [ - "#make columns that have hours of day and days of the week listed in numbers and names\n", + "#make bar graph to show total earnings per date\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" + "df_day_sum.iplot(kind = 'bar', x='shop_date', y='shop_date_total', \n", + " xTitle = 'Popup Shop Date', yTitle = 'Gross Earnings per Date',\n", + " title = 'Made in Baltimore Holiday Shop Earnings per Day',\n", + " text='day_name')" ] }, { - "cell_type": "code", - "execution_count": 22, + "cell_type": "markdown", "metadata": { - "collapsed": true + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": 0, + "height": 7, + "hidden": false, + "row": 16, + "width": 12 + }, + "report_default": { + "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" + "From this data, we see that December 16 (\\$4348.14), December 10 (\\$4231.02), and December 17 (\\$3262.01) were the highest-earning days for the Made in Baltimore Holiday Shop. Mayor Pugh and her staff visited the Holiday Shop on December 10, which could have contributed to higher foot traffic and, therefore, more purchases. Mayor Pugh also commended several Made in Baltimore businesses at her Wednesday, December 13th Press Conference, which could have led to the weekend of December 15-17, having the highest total sales for a weekend and December 16, having the highest sales for a single day.\n", + "

\n", + "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": 23, + "cell_type": "markdown", "metadata": { - "collapsed": true + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": 0, + "height": 3, + "hidden": false, + "row": 23, + "width": 5 + }, + "report_default": { + "hidden": false + } + } + } + } }, - "outputs": [], "source": [ - "#sum Gross earnings per shop date\n", - "df['shop_date_total']= df.groupby('shop_date')['Gross'].transform('sum')" + "### What days did the shop have the weakest sales?" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 32, "metadata": { - "collapsed": true + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } }, "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')" + "df_day_of_week=df\n", + "df_day_of_week['day_total']=df_day_of_week.groupby('day_name')['Gross'].transform('sum')" ] }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "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", - "
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"gridcolor": "#E1E5ED", + "showgrid": true, + "tickfont": { + "color": "#4D5663" + }, + "title": "Day of Week", + "titlefont": { + "color": "#4D5663" + }, + "zerolinecolor": "#E1E5ED" + }, + "yaxis1": { + "gridcolor": "#E1E5ED", + "showgrid": true, + "tickfont": { + "color": "#4D5663" + }, + "title": "Gross Earnings per Day of Week", + "titlefont": { + "color": "#4D5663" + }, + "zerolinecolor": "#E1E5ED" + } + } + }, + "text/html": [ + "
" ], - "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 " + "text/vnd.plotly.v1+html": [ + "
" ] }, - "execution_count": 30, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "df_day_sum" + "#make bar graph to show total earnings per date\n", + "\n", + "df_day_of_week.iplot(kind = 'bar', x='day_name', y='day_total', \n", + " xTitle = 'Day of Week', yTitle = 'Gross Earnings per Day of Week',\n", + " title = 'Made in Baltimore Holiday Shop Earnings per Day of Week',\n", + " text='day_name')" ] }, { - "cell_type": "code", - "execution_count": 26, + "cell_type": "markdown", "metadata": { - "scrolled": true + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": 0, + "height": 7, + "hidden": false, + "row": 26, + "width": 5 + }, + "report_default": { + "hidden": false + } + } + } + } }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/vnd.plotly.v1+html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/vnd.plotly.v1+html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "source": [ + "Saturday (\\$12,939.23) and Sunday (\\$12,528.68) had the strongest sales, however, the shop was only open for one Monday, Tuesday, Wednesday, and Thursday during the holiday popup duration. Although the holiday popup shop was open for five Saturdays, five Sundays, and four Fridays, which shows in the differences in earnings per day of week, the strongest sale dates were still on Saturdays and Sundays (as seen in the previous graph). This might suggest that the shop should open additional locations during weekends instead of having the one shop open during weekdays." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": 0, + "height": 4, + "hidden": false, + "row": 33, + "width": 4 + }, + "report_default": { + "hidden": false + } + } + } } - ], + }, "source": [ - "#make distribution visualizations in plotly\n", - "#import plotly\n", - "import plotly\n", - "from plotly.graph_objs import *\n", - "from plotly.offline import download_plotlyjs, init_notebook_mode, iplot\n", - "plotly.offline.init_notebook_mode()\n", - "import plotly.figure_factory as ff\n", - "\n", - "import cufflinks as cf\n", - "cf.go_offline()" + "### What time of day did the shop have the strongest sales?" ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, + "execution_count": 34, + "metadata": { + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } + }, + "outputs": [], + "source": [ + "df_time_of_day=df\n", + "df_time_of_day['hour_total']=df_time_of_day.groupby('hour')['Gross'].transform('sum')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": null, + "height": 19, + "hidden": false, + "row": 41, + "width": null + }, + "report_default": { + "hidden": false + } + } + } + } + }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/anaconda3/lib/python3.6/site-packages/cufflinks/plotlytools.py:156: FutureWarning:\n", - "\n", - "pandas.tslib is deprecated and will be removed in a future version.\n", - "You can access Timestamp as pandas.Timestamp\n", - "\n" - ] - }, { "data": { "application/vnd.plotly.v1+json": { 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" + "
" ], "text/vnd.plotly.v1+html": [ - "
" + "
" ] }, "metadata": {}, @@ -4388,106 +6709,56 @@ "source": [ "#make bar graph to show total earnings per date\n", "\n", - "df_day_sum.iplot(kind = 'bar', x='shop_date', y='shop_date_total', \n", - " xTitle = 'Popup Shop Date', yTitle = 'Gross Earnings per Date',\n", - " title = 'Made in Baltimore Holiday Shop Earnings per Day',\n", - " text='day_name')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From this data, we see that December 16 (\\$4348.14), December 10 (\\$4231.02), and December 17 (\\$3262.01) were the highest-earning days for the Made in Baltimore Holiday Shop. Mayor Pugh and her staff visited the Holiday Shop on December 10, which could have contributed to higher foot traffic and, therefore, more purchases. Mayor Pugh also commended several Made in Baltimore businesses at her Wednesday, December 13th Press Conference, which could have led to the weekend of December 15-17, having the highest total sales for a weekend and December 16, having the highest sales for a single day.\n", - "

\n", - "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 - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What days did the shop have the weakest sales?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What time of day did the shop have the strongest sales?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What time of day did the shop have the strongest sales?" + "df_time_of_day.iplot(kind = 'bar', x='hour', y='hour_total', \n", + " xTitle = 'Hour of Day', yTitle = 'Gross Earnings per Time of Day',\n", + " title = 'Made in Baltimore Holiday Shop Earnings per Hour in Day',\n", + " )" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Can we find any correlations between time or day and sales?" - ] - }, - { - "cell_type": "code", - "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "col": 0, + "height": 4, + "hidden": false, + "row": 37, + "width": 12 + }, + "report_default": { + "hidden": false + } + } + } + } }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, "source": [ - "### What additional data would help us determine marketing outreach and increase sales for next year's Holiday Shop?" + "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": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } }, "outputs": [], "source": [] @@ -4496,7 +6767,20 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } }, "outputs": [], "source": [] @@ -4505,13 +6789,45 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "extensions": { + "jupyter_dashboards": { + "version": 1, + "views": { + "grid_default": { + "hidden": true + }, + "report_default": { + "hidden": true + } + } + } + } }, "outputs": [], "source": [] } ], "metadata": { + "extensions": { + "jupyter_dashboards": { + "activeView": "report_default", + "version": 1, + "views": { + "grid_default": { + "cellMargin": 10, + "defaultCellHeight": 20, + "maxColumns": 12, + "name": "grid", + "type": "grid" + }, + "report_default": { + "name": "report", + "type": "report" + } + } + } + }, "kernelspec": { "display_name": "Python 3", "language": "python",