{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd \n", "\n", "#create new data frame reading in intake data csv file\n", "intakedata = pd.read_csv('drewsdata/intakedata20181-20193.csv')\n", "\n", "#drop null value columns to avoid errors\n", "intakedata .dropna(inplace = True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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QuarterWeekIntakes
0Fall 2018117
1Fall 2018297
2Fall 2018348
3Fall 2018432
4Fall 2018523
5Fall 201869
6Fall 2018728
7Fall 2018855
8Fall 2018912
9Fall 20181025
10Fall 20181187
11Fall 20181279
12Winter 2019157
13Winter 2019256
14Winter 2019395
15Winter 2019489
16Winter 2019565
17Winter 2019641
18Winter 2019723
19Winter 2019852
20Winter 2019926
21Winter 20191065
22Winter 20191196
23Winter 20191246
24Spring 201915
25Spring 2019215
26Spring 2019356
27Spring 2019411
28Spring 2019598
29Spring 2019644
30Spring 2019793
31Spring 2019875
32Spring 2019940
33Spring 20191022
34Spring 20191121
35Spring 20191240
\n", "
" ], "text/plain": [ " Quarter Week Intakes\n", "0 Fall 2018 1 17\n", "1 Fall 2018 2 97\n", "2 Fall 2018 3 48\n", "3 Fall 2018 4 32\n", "4 Fall 2018 5 23\n", "5 Fall 2018 6 9\n", "6 Fall 2018 7 28\n", "7 Fall 2018 8 55\n", "8 Fall 2018 9 12\n", "9 Fall 2018 10 25\n", "10 Fall 2018 11 87\n", "11 Fall 2018 12 79\n", "12 Winter 2019 1 57\n", "13 Winter 2019 2 56\n", "14 Winter 2019 3 95\n", "15 Winter 2019 4 89\n", "16 Winter 2019 5 65\n", "17 Winter 2019 6 41\n", "18 Winter 2019 7 23\n", "19 Winter 2019 8 52\n", "20 Winter 2019 9 26\n", "21 Winter 2019 10 65\n", "22 Winter 2019 11 96\n", "23 Winter 2019 12 46\n", "24 Spring 2019 1 5\n", "25 Spring 2019 2 15\n", "26 Spring 2019 3 56\n", "27 Spring 2019 4 11\n", "28 Spring 2019 5 98\n", "29 Spring 2019 6 44\n", "30 Spring 2019 7 93\n", "31 Spring 2019 8 75\n", "32 Spring 2019 9 40\n", "33 Spring 2019 10 22\n", "34 Spring 2019 11 21\n", "35 Spring 2019 12 40" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(intakedata)\n", "type(intakedata)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Quarter object\n", "Week int64\n", "Intakes int64\n", "dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#check data type in each column\n", "#are my numbers actually stored as integers?\n", "intakedata.dtypes" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Fall 2018\n", "1 Fall 2018\n", "2 Fall 2018\n", "3 Fall 2018\n", "4 Fall 2018\n", "5 Fall 2018\n", "6 Fall 2018\n", "7 Fall 2018\n", "8 Fall 2018\n", "9 Fall 2018\n", "10 Fall 2018\n", "11 Fall 2018\n", "12 Winter 2019\n", "13 Winter 2019\n", "14 Winter 2019\n", "15 Winter 2019\n", "16 Winter 2019\n", "17 Winter 2019\n", "18 Winter 2019\n", "19 Winter 2019\n", "20 Winter 2019\n", "21 Winter 2019\n", "22 Winter 2019\n", "23 Winter 2019\n", "24 Spring 2019\n", "25 Spring 2019\n", "26 Spring 2019\n", "27 Spring 2019\n", "28 Spring 2019\n", "29 Spring 2019\n", "30 Spring 2019\n", "31 Spring 2019\n", "32 Spring 2019\n", "33 Spring 2019\n", "34 Spring 2019\n", "35 Spring 2019\n", "Name: Quarter, dtype: object" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#take off year from quarter values, e.g., take off 2018, 2019 from Fall, Winter, Spring\n", "\n", "#create new object quarter_without_year to contain only quarter values\n", "quarter = intakedata['Quarter']\n", "display(quarter)\n", "type(quarter)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Fall',\n", " 'Fall',\n", " 'Fall',\n", " 'Fall',\n", " 'Fall',\n", " 'Fall',\n", " 'Fall',\n", " 'Fall',\n", " 'Fall',\n", " 'Fall',\n", " 'Fall',\n", " 'Fall',\n", " 'Winter',\n", " 'Winter',\n", " 'Winter',\n", " 'Winter',\n", " 'Winter',\n", " 'Winter',\n", " 'Winter',\n", " 'Winter',\n", " 'Winter',\n", " 'Winter',\n", " 'Winter',\n", " 'Winter',\n", " 'Spring',\n", " 'Spring',\n", " 'Spring',\n", " 'Spring',\n", " 'Spring',\n", " 'Spring',\n", " 'Spring',\n", " 'Spring',\n", " 'Spring',\n", " 'Spring',\n", " 'Spring',\n", " 'Spring']" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#cut off last five characters in each row of quarter, so we just have Fall, Winter, Spring\n", "quarter_no_year = [x[:-5] for x in quarter]\n", "display(quarter_no_year)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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QuarterWeekIntakes
0Fall117
1Fall297
2Fall348
3Fall432
4Fall523
5Fall69
6Fall728
7Fall855
8Fall912
9Fall1025
10Fall1187
11Fall1279
12Winter157
13Winter256
14Winter395
15Winter489
16Winter565
17Winter641
18Winter723
19Winter852
20Winter926
21Winter1065
22Winter1196
23Winter1246
24Spring15
25Spring215
26Spring356
27Spring411
28Spring598
29Spring644
30Spring793
31Spring875
32Spring940
33Spring1022
34Spring1121
35Spring1240
\n", "
" ], "text/plain": [ " Quarter Week Intakes\n", "0 Fall 1 17\n", "1 Fall 2 97\n", "2 Fall 3 48\n", "3 Fall 4 32\n", "4 Fall 5 23\n", "5 Fall 6 9\n", "6 Fall 7 28\n", "7 Fall 8 55\n", "8 Fall 9 12\n", "9 Fall 10 25\n", "10 Fall 11 87\n", "11 Fall 12 79\n", "12 Winter 1 57\n", "13 Winter 2 56\n", "14 Winter 3 95\n", "15 Winter 4 89\n", "16 Winter 5 65\n", "17 Winter 6 41\n", "18 Winter 7 23\n", "19 Winter 8 52\n", "20 Winter 9 26\n", "21 Winter 10 65\n", "22 Winter 11 96\n", "23 Winter 12 46\n", "24 Spring 1 5\n", "25 Spring 2 15\n", "26 Spring 3 56\n", "27 Spring 4 11\n", "28 Spring 5 98\n", "29 Spring 6 44\n", "30 Spring 7 93\n", "31 Spring 8 75\n", "32 Spring 9 40\n", "33 Spring 10 22\n", "34 Spring 11 21\n", "35 Spring 12 40" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#add quarter_no_year back into intakedata, replacing its original Quarter column\n", "intakedata['Quarter'] = quarter_no_year\n", "display(intakedata)\n", "type(intakedata)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "36" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#now that we've finished our data cleanup\n", "#lets add the two columns to enter # clinicians and #intakes/per clinican\n", "clinicians = [0]*len(intakedata)\n", "#display(clinicians)\n", "len(clinicians)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "intakes_per_clinicians = [0]*len(intakedata)\n", "#display(intakes_per_clinicians)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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QuarterWeekIntakesCliniciansIntakes Per Clinicians
0Fall11700
1Fall29700
2Fall34800
3Fall43200
4Fall52300
5Fall6900
6Fall72800
7Fall85500
8Fall91200
9Fall102500
10Fall118700
11Fall127900
12Winter15700
13Winter25600
14Winter39500
15Winter48900
16Winter56500
17Winter64100
18Winter72300
19Winter85200
20Winter92600
21Winter106500
22Winter119600
23Winter124600
24Spring1500
25Spring21500
26Spring35600
27Spring41100
28Spring59800
29Spring64400
30Spring79300
31Spring87500
32Spring94000
33Spring102200
34Spring112100
35Spring124000
\n", "
" ], "text/plain": [ " Quarter Week Intakes Clinicians Intakes Per Clinicians\n", "0 Fall 1 17 0 0\n", "1 Fall 2 97 0 0\n", "2 Fall 3 48 0 0\n", "3 Fall 4 32 0 0\n", "4 Fall 5 23 0 0\n", "5 Fall 6 9 0 0\n", "6 Fall 7 28 0 0\n", "7 Fall 8 55 0 0\n", "8 Fall 9 12 0 0\n", "9 Fall 10 25 0 0\n", "10 Fall 11 87 0 0\n", "11 Fall 12 79 0 0\n", "12 Winter 1 57 0 0\n", "13 Winter 2 56 0 0\n", "14 Winter 3 95 0 0\n", "15 Winter 4 89 0 0\n", "16 Winter 5 65 0 0\n", "17 Winter 6 41 0 0\n", "18 Winter 7 23 0 0\n", "19 Winter 8 52 0 0\n", "20 Winter 9 26 0 0\n", "21 Winter 10 65 0 0\n", "22 Winter 11 96 0 0\n", "23 Winter 12 46 0 0\n", "24 Spring 1 5 0 0\n", "25 Spring 2 15 0 0\n", "26 Spring 3 56 0 0\n", "27 Spring 4 11 0 0\n", "28 Spring 5 98 0 0\n", "29 Spring 6 44 0 0\n", "30 Spring 7 93 0 0\n", "31 Spring 8 75 0 0\n", "32 Spring 9 40 0 0\n", "33 Spring 10 22 0 0\n", "34 Spring 11 21 0 0\n", "35 Spring 12 40 0 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "intakedata['Clinicians'] = clinicians\n", "intakedata['Intakes Per Clinicians'] = intakes_per_clinicians\n", "#intakedata['Clinics'] = [0]*len(intakedata)\n", "#del(intakedata['Clinics'])\n", "display(intakedata)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdin", "output_type": "stream", "text": [ "Enter Quarter: Fall, Winter, or Spring Spring\n", "Enter week: 1-12 12\n", "Enter Number of Clinicians 38\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Printing Quarter, Week, # Clinicians\n", "Spring 12 38\n" ] } ], "source": [ "#ask for quarter, week, and # of clinicians\n", "input_quarter = input('Enter Quarter: Fall, Winter, or Spring')\n", "input_week = input('Enter week: 1-12')\n", "input_clinicians = input('Enter Number of Clinicians')\n", "\n", "print('Printing Quarter, Week, # Clinicians')\n", "print(input_quarter, input_week, input_clinicians)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#sanity check, what is the type of input_quarter?\n", "#type(input_quarter)\n", "type(input_clinicians) #string, not integer. need to convert to integer later" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", "5 False\n", "6 False\n", "7 False\n", "8 False\n", "9 False\n", "10 False\n", "11 False\n", "12 False\n", "13 False\n", "14 False\n", "15 False\n", "16 False\n", "17 False\n", "18 False\n", "19 False\n", "20 False\n", "21 False\n", "22 False\n", "23 False\n", "24 True\n", "25 True\n", "26 True\n", "27 True\n", "28 True\n", "29 True\n", "30 True\n", "31 True\n", "32 True\n", "33 True\n", "34 True\n", "35 True\n", "Name: Quarter, dtype: bool\n" ] } ], "source": [ "#let's look at quarter column and match to input_quarter\n", "#in this case, let's grab all our spring values, should be 12 rows\n", "match_quarter = intakedata['Quarter'] == input_quarter\n", "print(match_quarter)\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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WeekIntakesCliniciansIntakes Per Clinicians
241500
2521500
2635600
2741100
2859800
2964400
3079300
3187500
3294000
33102200
34112100
35124000
\n", "
" ], "text/plain": [ " Week Intakes Clinicians Intakes Per Clinicians\n", "24 1 5 0 0\n", "25 2 15 0 0\n", "26 3 56 0 0\n", "27 4 11 0 0\n", "28 5 98 0 0\n", "29 6 44 0 0\n", "30 7 93 0 0\n", "31 8 75 0 0\n", "32 9 40 0 0\n", "33 10 22 0 0\n", "34 11 21 0 0\n", "35 12 40 0 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#creating new dataframe with TRUE values within intakedata, \n", "#here it is looking for rows 24-35\n", "intake_match_quarter = intakedata[match_quarter]\n", "#display(intake_match_quarter)\n", "\n", "#look at column headings\n", "#intake_match_quarter.columns\n", "#type(intake_match_quarter)\n", "\n", "intake_match_quarter = pd.DataFrame(intake_match_quarter, columns=['Week','Intakes','Clinicians',\n", " 'Intakes Per Clinicians'])\n", "display(intake_match_quarter)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12\n" ] }, { "data": { "text/plain": [ "int" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#now let's look at week, isolate by input_week row\n", "print(input_week)\n", "type(input_week)\n", "\n", "#convert input_week from string to integer\n", "input_week = int(input_week)\n", "type(input_week)\n" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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WeekIntakesCliniciansIntakes Per Clinicians
35124000
\n", "
" ], "text/plain": [ " Week Intakes Clinicians Intakes Per Clinicians\n", "35 12 40 0 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#create new dataframe copy of intake_match_quarter, filter by week\n", "match_week = intake_match_quarter['Week'] == input_week #which rows match input_week=12\n", "type(match_week)\n", "#display(match_week)\n", "#create new data frame intake_match_week to house my input_week=12 match\n", "intake_match_week = intake_match_quarter[match_week]\n", "display(intake_match_week)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IntakesCliniciansIntakes Per Clinicians
354000
\n", "
" ], "text/plain": [ " Intakes Clinicians Intakes Per Clinicians\n", "35 40 0 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "newintake = pd.DataFrame(intake_match_week, columns = ['Intakes','Clinicians','Intakes Per Clinicians'])\n", "display(newintake)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "type(input_clinicians)\n", "input_clinicians = int(input_clinicians)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IntakesCliniciansIntakes Per Clinicians
3540380
\n", "
" ], "text/plain": [ " Intakes Clinicians Intakes Per Clinicians\n", "35 40 38 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "newintake['Clinicians'] = input_clinicians\n", "display(newintake)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IntakesCliniciansIntakes Per Clinicians
3540381.052632
\n", "
" ], "text/plain": [ " Intakes Clinicians Intakes Per Clinicians\n", "35 40 38 1.052632" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "newintake['Intakes Per Clinicians'] = newintake['Intakes'] / newintake['Clinicians']\n", "display(newintake)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Quarter: Spring\n", "Week: 12\n", "#Clinicans: 38\n", "Intakes Per Clinicians 1.0526315789473684\n" ] } ], "source": [ "print(\"Quarter:\", input_quarter)\n", "print(\"Week:\", input_week)\n", "print(\"#Clinicans:\", input_clinicians)\n", "print(\"Intakes Per Clinicians\", float(newintake['Intakes Per Clinicians']))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 2 }