{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "series = 'K1145'" ] }, { "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 K1145

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

Certificates of Exemption from Dictation Test, annual certificate number order

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Total items4,816
Access status
Open4,791 (99.48%)
Not yet examined25 (0.52%)
Number of items digitised175 (3.63%)
Number of pages digitised874
Date of earliest content1900
Date of latest content1955

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
01719426K11451900/95Ah Kin [Chinese]1900 - 19001900-01-01 00:00:001900-01-01 00:00:00OpenPerthFalse0
11719427K11451900/116Ah Leck [Chinese]1900 - 19001900-01-01 00:00:001900-01-01 00:00:00OpenPerthFalse0
21719428K11451900/144Ah Shim [Chinese]1900 - 19001900-01-01 00:00:001900-01-01 00:00:00OpenPerthFalse0
31719429K11451900/165Mahomet Rasool [Afghan]1900 - 19001900-01-01 00:00:001900-01-01 00:00:00OpenPerthFalse0
41719431K11451900/169Lee Yacke [Chinese]1900 - 19001900-01-01 00:00:001900-01-01 00:00:00OpenPerthFalse0
" ], "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": [ 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1947 ], "y": [ 2, 10, 4, 4, 4, 5, 6, 8, 4, 15, 3, 7, 6, 4, 6, 2, 3, 4, 7, 3, 2, 2, 6, 3, 7, 5, 6, 9, 1, 3, 7, 3, 2, 2, 6, 1, 11, 2, 1, 3, 1 ] }, { "name": "Not digitised", "type": "bar", "x": [ 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955 ], "y": [ 42, 80, 209, 113, 127, 152, 195, 219, 164, 205, 232, 238, 281, 237, 210, 200, 206, 159, 168, 200, 225, 196, 139, 139, 112, 131, 122, 117, 109, 117, 104, 77, 56, 61, 68, 72, 50, 51, 46, 34, 46, 26, 26, 26, 26, 26, 27, 39, 27, 27, 28, 27, 27, 27, 27, 27 ] } ], "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
2chinese3,293
0ah1,064
90japanese708
30indian593
8lee321
57fong222
139wong192
12sing158
7afghan144
122singh131
54chen123
40chong98
172chung89
44wing88
75yee78
167chew78
210mahomed73
67wah72
157chin69
71kee68
66hong63
34khan61
323sam58
79hing57
673born49
" ], "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": [ { "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
0chinese ah741
1chinese lee199
2singh indian129
3japanese ah122
4sing chinese110
5chinese wong106
6indian ah91
7fong chinese78
8chinese fong78
9chinese chen77
10chong chinese70
11you chinese66
12kee chinese62
13lee chinese53
14wah chinese50
15hong chinese50
16wing chinese49
17chinese chung47
18hing chinese46
19shing chinese45
20ah sing45
21bux indian43
22chinese yee42
23chew chinese41
24sam chinese40
" ], "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": 10, "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 }