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{ | ||
"cells": [ | ||
{ | ||
"attachments": {}, | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# StringConcatenator\n", | ||
"This notebook shows the functionality of the `StringConcatenator` class. This Transformer combines data from specified columns, of mixed datatypes, into a new column containing one string." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 1, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import pandas as pd\n", | ||
"import numpy as np" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 2, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import tubular\n", | ||
"from tubular.strings import StringConcatenator" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 3, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"data": { | ||
"text/plain": [ | ||
"'0.3.3'" | ||
] | ||
}, | ||
"execution_count": 3, | ||
"metadata": {}, | ||
"output_type": "execute_result" | ||
} | ||
], | ||
"source": [ | ||
"tubular.__version__" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Create sample data" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 4, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"character_df = pd.DataFrame(([':', ')', 5], [':', '(', 3]), columns=['c1', 'c2', 'c3'])" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 5, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"data": { | ||
"text/html": [ | ||
"<div>\n", | ||
"<style scoped>\n", | ||
" .dataframe tbody tr th:only-of-type {\n", | ||
" vertical-align: middle;\n", | ||
" }\n", | ||
"\n", | ||
" .dataframe tbody tr th {\n", | ||
" vertical-align: top;\n", | ||
" }\n", | ||
"\n", | ||
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" <th>c1</th>\n", | ||
" <th>c2</th>\n", | ||
" <th>c3</th>\n", | ||
" </tr>\n", | ||
" </thead>\n", | ||
" <tbody>\n", | ||
" <tr>\n", | ||
" <th>0</th>\n", | ||
" <td>:</td>\n", | ||
" <td>)</td>\n", | ||
" <td>5</td>\n", | ||
" </tr>\n", | ||
" <tr>\n", | ||
" <th>1</th>\n", | ||
" <td>:</td>\n", | ||
" <td>(</td>\n", | ||
" <td>3</td>\n", | ||
" </tr>\n", | ||
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"</table>\n", | ||
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], | ||
"text/plain": [ | ||
" c1 c2 c3\n", | ||
"0 : ) 5\n", | ||
"1 : ( 3" | ||
] | ||
}, | ||
"execution_count": 5, | ||
"metadata": {}, | ||
"output_type": "execute_result" | ||
} | ||
], | ||
"source": [ | ||
"character_df.head()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Simple usage" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"### Initialising StringConcatenatorTransformer" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"The user must specify the following;\n", | ||
"- `columns` giving the columns to join\n", | ||
"- `new column` giving the name of the new column\n", | ||
"- `separator` giving the separator for joining (optional)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 6, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"to_merge = ['c1', 'c2', 'c3']\n", | ||
"\n", | ||
"join_columns = StringConcatenator(to_merge, \"merged\", \"-\")" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"### StringConcatenatorTransformer fit\n", | ||
"There is no fit method for the StringConcatenatorTransformer as the methods that it can run do not 'learn' anything from the data." | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"### StringConcatenatorTransformer transform" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 7, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"joined_df = join_columns.transform(character_df)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 8, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"data": { | ||
"text/html": [ | ||
"<div>\n", | ||
"<style scoped>\n", | ||
" .dataframe tbody tr th:only-of-type {\n", | ||
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" }\n", | ||
"\n", | ||
" .dataframe tbody tr th {\n", | ||
" vertical-align: top;\n", | ||
" }\n", | ||
"\n", | ||
" .dataframe thead th {\n", | ||
" text-align: right;\n", | ||
" }\n", | ||
"</style>\n", | ||
"<table border=\"1\" class=\"dataframe\">\n", | ||
" <thead>\n", | ||
" <tr style=\"text-align: right;\">\n", | ||
" <th></th>\n", | ||
" <th>c1</th>\n", | ||
" <th>c2</th>\n", | ||
" <th>c3</th>\n", | ||
" <th>merged</th>\n", | ||
" </tr>\n", | ||
" </thead>\n", | ||
" <tbody>\n", | ||
" <tr>\n", | ||
" <th>0</th>\n", | ||
" <td>:</td>\n", | ||
" <td>)</td>\n", | ||
" <td>5</td>\n", | ||
" <td>:-)-5</td>\n", | ||
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" <tr>\n", | ||
" <th>1</th>\n", | ||
" <td>:</td>\n", | ||
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" <td>3</td>\n", | ||
" <td>:-(-3</td>\n", | ||
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"text/plain": [ | ||
" c1 c2 c3 merged\n", | ||
"0 : ) 5 :-)-5\n", | ||
"1 : ( 3 :-(-3" | ||
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}, | ||
"execution_count": 8, | ||
"metadata": {}, | ||
"output_type": "execute_result" | ||
} | ||
], | ||
"source": [ | ||
"joined_df" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3.8.0 ('tubular-dev')", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
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"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.8.0" | ||
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"toc": { | ||
"base_numbering": 1, | ||
"nav_menu": {}, | ||
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"sideBar": true, | ||
"skip_h1_title": false, | ||
"title_cell": "Table of Contents", | ||
"title_sidebar": "Contents", | ||
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"left": "10px", | ||
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"toc_section_display": true, | ||
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"vscode": { | ||
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"hash": "cc666868ff21538e6058ba6d4768423bd0d0d7d7fded3ffb1bc309a0bf9339c2" | ||
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"nbformat": 4, | ||
"nbformat_minor": 2 | ||
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