{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "series = 'SP11/26'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "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": [ "import os\n", "import pandas as pd\n", "import series_details\n", "import plotly.offline as py\n", "py.init_notebook_mode()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(os.path.join('data', '{}.csv'.format(series.replace('/', '-'))), parse_dates=['start_date', 'end_date'])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "

National Archives of Australia: Series SP11/26

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

Applications for Certificates of Domicile

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Total items27
Access status
Open27 (100.00%)
Number of items digitised5 (18.52%)
Number of pages digitised84
Date of earliest content1902
Date of latest content1902

Download the complete CSV file

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "series_details.display_summary(series, df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Content preview" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
identifierseriescontrol_symboltitlecontents_datesstart_dateend_dateaccess_statuslocationdigitised_statusdigitised_pages
01146999SP11/26A1William Ah Bow1902 - 19021902-01-01 00:00:001902-01-01 00:00:00OpenSydneyFalse0
11511061SP11/26A2/69Ah Mung, Fong Tung1902 - 19021902-01-01 00:00:001902-01-01 00:00:00OpenSydneyFalse0
21511086SP11/26A3/70123Bez Mahomet, Zareen, Doz Mahomet, Adam Khan [4 Afghans]1902 - 19021902-01-01 00:00:001902-01-01 00:00:00OpenSydneyFalse0
31511100SP11/26A4/74Ah Kee1902 - 19021902-01-01 00:00:001902-01-01 00:00:00OpenSydneyFalse0
41511125SP11/26A5/54Ah Chong1902 - 19021902-01-01 00:00:001902-01-01 00:00:00OpenSydneyFalse0
" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Change the number_of_rows value to see more\n", "number_of_rows = 5\n", "\n", "# Display dataframe \n", "df[:number_of_rows].style.set_properties(['title'], **{'text-align': 'left'}).set_table_styles([dict(selector=\"th\", props=[(\"text-align\", \"center\")]),\n", " dict(selector='.row_heading, .blank', props=[('display', 'none')])])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot content dates" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "name": "Digitised", "type": "bar", "x": [ 1902 ], "y": [ 5 ] }, { "name": "Not digitised", "type": "bar", "x": [ 1902 ], "y": [ 22 ] } ], "layout": { "barmode": "stack", "title": "Content dates", "xaxis": { "title": "Year" }, "yaxis": { "title": "Number of items" } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = series_details.plot_dates(df)\n", "py.iplot(fig, filename='series-dates-bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## View word frequencies" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Combine all of the file titles into a single string\n", "title_text = a = df['title'].str.lower().str.cat(sep=' ')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
wordcount
1ah9
31jimmy2
37kum2
21sing2
49lee2
7mahomet2
28wing2
5tung2
4fong2
36yee2
38mock1
46foon1
39h1
40w1
41g1
42ky1
43ling1
44kday1
45loo1
48myerson1
47leon1
34james1
50shing1
51low1
52soo1
" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "series_details.display_word_counts(title_text)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/tim/mycode/naa-data-wap/lib/python3.6/site-packages/pandas/io/formats/style.py:939: RuntimeWarning:\n", "\n", "divide by zero encountered in long_scalars\n", "\n" ] }, { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ngramcount
0doz mahomet1
1fong tung1
2ling kday1
3zareen doz1
4loo foon1
5fong sing1
6mahomet zareen1
7hack walter1
8sing bedi1
9w g1
10mar hong1
11yee kum1
12myerson lee1
13tung hack1
14shing low1
15ah wing1
16loong sing1
17ah bow1
18billy loong1
19jimmy chuck1
20ah kee1
21bedi joseph1
22kee ah1
23ky ling1
24low kum1
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Change ngram_count for larger ngrams (trigrams etc)\n", "ngram_count = 2\n", "series_details.display_top_ngrams(title_text, ngram_count)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }