{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import eland as ed\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Fix console size for consistent test results\n", "from eland.conftest import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Online Retail Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting Started\n", "\n", "To get started, let's create an `eland.DataFrame` by reading a csv file. This creates and populates the \n", "`online-retail` index in the local Elasticsearch cluster." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df = ed.read_csv(\"data/online-retail.csv.gz\",\n", " es_client='localhost', \n", " es_dest_index='online-retail', \n", " es_if_exists='replace', \n", " es_dropna=True,\n", " es_refresh=True,\n", " compression='gzip',\n", " index_col=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we see that the `\"_id\"` field was used to index our data frame. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'_id'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.index.index_field" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we can check which field from elasticsearch are available to our eland data frame. `columns` is available as a parameter when instantiating the data frame which allows one to choose only a subset of fields from your index to be included in the data frame. Since we didn't set this parameter, we have access to all fields." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode',\n", " 'UnitPrice'],\n", " dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's see the data types of our fields. Running `df.dtypes`, we can see that elasticsearch field types are mapped to pandas field types." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Country object\n", "CustomerID float64\n", "Description object\n", "InvoiceDate object\n", "InvoiceNo object\n", "Quantity int64\n", "StockCode object\n", "UnitPrice float64\n", "dtype: object" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also offer a `.info_es()` data frame method that shows all info about the underlying index. It also contains information about operations being passed from data frame methods to elasticsearch. More on this later." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "index_pattern: online-retail\n", "Index:\n", " index_field: _id\n", " is_source_field: False\n", "Mappings:\n", " capabilities:\n", " es_field_name is_source es_dtype es_date_format pd_dtype is_searchable is_aggregatable is_scripted aggregatable_es_field_name\n", "Country Country True keyword None object True True False Country\n", "CustomerID CustomerID True double None float64 True True False CustomerID\n", "Description Description True keyword None object True True False Description\n", "InvoiceDate InvoiceDate True keyword None object True True False InvoiceDate\n", "InvoiceNo InvoiceNo True keyword None object True True False InvoiceNo\n", "Quantity Quantity True long None int64 True True False Quantity\n", "StockCode StockCode True keyword None object True True False StockCode\n", "UnitPrice UnitPrice True double None float64 True True False UnitPrice\n", "Operations:\n", " tasks: []\n", " size: None\n", " sort_params: None\n", " _source: ['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode', 'UnitPrice']\n", " body: {}\n", " post_processing: []\n", "\n" ] } ], "source": [ "print(df.info_es())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Selecting and Indexing Data\n", "\n", "Now that we understand how to create a data frame and get access to it's underlying attributes, let's see how we can select subsets of our data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### head and tail\n", "\n", "much like pandas, eland data frames offer `.head(n)` and `.tail(n)` methods that return the first and last n rows, respectively." ] }, { "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", "
CountryCustomerID...StockCodeUnitPrice
1000United Kingdom14729.0...211231.25
1001United Kingdom14729.0...211241.25
\n", "
\n", "

2 rows × 8 columns

" ], "text/plain": [ " Country CustomerID ... StockCode UnitPrice\n", "1000 United Kingdom 14729.0 ... 21123 1.25\n", "1001 United Kingdom 14729.0 ... 21124 1.25\n", "\n", "[2 rows x 8 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(2)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "index_pattern: online-retail\n", "Index:\n", " index_field: _id\n", " is_source_field: False\n", "Mappings:\n", " capabilities:\n", " es_field_name is_source es_dtype es_date_format pd_dtype is_searchable is_aggregatable is_scripted aggregatable_es_field_name\n", "Country Country True keyword None object True True False Country\n", "CustomerID CustomerID True double None float64 True True False CustomerID\n", "Description Description True keyword None object True True False Description\n", "InvoiceDate InvoiceDate True keyword None object True True False InvoiceDate\n", "InvoiceNo InvoiceNo True keyword None object True True False InvoiceNo\n", "Quantity Quantity True long None int64 True True False Quantity\n", "StockCode StockCode True keyword None object True True False StockCode\n", "UnitPrice UnitPrice True double None float64 True True False UnitPrice\n", "Operations:\n", " tasks: [('tail': ('sort_field': '_doc', 'count': 2)), ('head': ('sort_field': '_doc', 'count': 2)), ('tail': ('sort_field': '_doc', 'count': 2))]\n", " size: 2\n", " sort_params: _doc:desc\n", " _source: ['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode', 'UnitPrice']\n", " body: {}\n", " post_processing: [('sort_index'), ('head': ('count': 2)), ('tail': ('count': 2))]\n", "\n" ] } ], "source": [ "print(df.tail(2).head(2).tail(2).info_es())" ] }, { "cell_type": "code", "execution_count": 9, "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", "
CountryCustomerID...StockCodeUnitPrice
14998United Kingdom17419.0...217731.25
14999United Kingdom17419.0...221492.10
\n", "
\n", "

2 rows × 8 columns

" ], "text/plain": [ " Country CustomerID ... StockCode UnitPrice\n", "14998 United Kingdom 17419.0 ... 21773 1.25\n", "14999 United Kingdom 17419.0 ... 22149 2.10\n", "\n", "[2 rows x 8 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### selecting columns\n", "\n", "you can also pass a list of columns to select columns from the data frame in a specified order." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryInvoiceDate
1000United Kingdom2010-12-01 12:43:00
1001United Kingdom2010-12-01 12:43:00
1002United Kingdom2010-12-01 12:43:00
1003United Kingdom2010-12-01 12:43:00
1004United Kingdom2010-12-01 12:43:00
\n", "
\n", "

5 rows × 2 columns

" ], "text/plain": [ " Country InvoiceDate\n", "1000 United Kingdom 2010-12-01 12:43:00\n", "1001 United Kingdom 2010-12-01 12:43:00\n", "1002 United Kingdom 2010-12-01 12:43:00\n", "1003 United Kingdom 2010-12-01 12:43:00\n", "1004 United Kingdom 2010-12-01 12:43:00\n", "\n", "[5 rows x 2 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['Country', 'InvoiceDate']].head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Boolean Indexing\n", "\n", "we also allow you to filter the data frame using boolean indexing. Under the hood, a boolean index maps to a `terms` query that is then passed to elasticsearch to filter the index." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'term': {'Country': 'Germany'}}\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", "
CountryCustomerID...StockCodeUnitPrice
1109Germany12662.0...228092.95
1110Germany12662.0...843472.55
1111Germany12662.0...849450.85
1112Germany12662.0...222421.65
1113Germany12662.0...222441.95
\n", "
\n", "

5 rows × 8 columns

" ], "text/plain": [ " Country CustomerID ... StockCode UnitPrice\n", "1109 Germany 12662.0 ... 22809 2.95\n", "1110 Germany 12662.0 ... 84347 2.55\n", "1111 Germany 12662.0 ... 84945 0.85\n", "1112 Germany 12662.0 ... 22242 1.65\n", "1113 Germany 12662.0 ... 22244 1.95\n", "\n", "[5 rows x 8 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# the construction of a boolean vector maps directly to an elasticsearch query\n", "print(df['Country']=='Germany')\n", "df[(df['Country']=='Germany')].head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we can also filter the data frame using a list of values." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'terms': {'Country': ['Germany', 'United States']}}\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", "
CountryCustomerID...StockCodeUnitPrice
1000United Kingdom14729.0...211231.25
1001United Kingdom14729.0...211241.25
1002United Kingdom14729.0...211221.25
1003United Kingdom14729.0...843781.25
1004United Kingdom14729.0...219850.29
\n", "
\n", "

5 rows × 8 columns

" ], "text/plain": [ " Country CustomerID ... StockCode UnitPrice\n", "1000 United Kingdom 14729.0 ... 21123 1.25\n", "1001 United Kingdom 14729.0 ... 21124 1.25\n", "1002 United Kingdom 14729.0 ... 21122 1.25\n", "1003 United Kingdom 14729.0 ... 84378 1.25\n", "1004 United Kingdom 14729.0 ... 21985 0.29\n", "\n", "[5 rows x 8 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(df['Country'].isin(['Germany', 'United States']))\n", "df[df['Country'].isin(['Germany', 'United Kingdom'])].head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also combine boolean vectors to further filter the data frame." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CountryCustomerID...StockCodeUnitPrice
\n", "
\n", "

0 rows × 8 columns

" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Country, CustomerID, Description, InvoiceDate, InvoiceNo, Quantity, StockCode, UnitPrice]\n", "Index: []\n", "\n", "[0 rows x 8 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[(df['Country']=='Germany') & (df['Quantity']>90)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using this example, let see how eland translates this boolean filter to an elasticsearch `bool` query." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "index_pattern: online-retail\n", "Index:\n", " index_field: _id\n", " is_source_field: False\n", "Mappings:\n", " capabilities:\n", " es_field_name is_source es_dtype es_date_format pd_dtype is_searchable is_aggregatable is_scripted aggregatable_es_field_name\n", "Country Country True keyword None object True True False Country\n", "CustomerID CustomerID True double None float64 True True False CustomerID\n", "Description Description True keyword None object True True False Description\n", "InvoiceDate InvoiceDate True keyword None object True True False InvoiceDate\n", "InvoiceNo InvoiceNo True keyword None object True True False InvoiceNo\n", "Quantity Quantity True long None int64 True True False Quantity\n", "StockCode StockCode True keyword None object True True False StockCode\n", "UnitPrice UnitPrice True double None float64 True True False UnitPrice\n", "Operations:\n", " tasks: [('boolean_filter': ('boolean_filter': {'bool': {'must': [{'term': {'Country': 'Germany'}}, {'range': {'Quantity': {'gt': 90}}}]}}))]\n", " size: None\n", " sort_params: None\n", " _source: ['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode', 'UnitPrice']\n", " body: {'query': {'bool': {'must': [{'term': {'Country': 'Germany'}}, {'range': {'Quantity': {'gt': 90}}}]}}}\n", " post_processing: []\n", "\n" ] } ], "source": [ "print(df[(df['Country']=='Germany') & (df['Quantity']>90)].info_es())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aggregation and Descriptive Statistics\n", "\n", "Let's begin to ask some questions of our data and use eland to get the answers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**How many different countries are there?**" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "16" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Country'].nunique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**What is the total sum of products ordered?**" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "111960.0" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Quantity'].sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Show me the sum, mean, min, and max of the qunatity and unit_price fields**" ] }, { "cell_type": "code", "execution_count": 17, "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", "
QuantityUnitPrice
sum111960.00061548.490000
mean7.4644.103233
max2880.000950.990000
min-9360.0000.000000
\n", "
" ], "text/plain": [ " Quantity UnitPrice\n", "sum 111960.000 61548.490000\n", "mean 7.464 4.103233\n", "max 2880.000 950.990000\n", "min -9360.000 0.000000" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['Quantity','UnitPrice']].agg(['sum', 'mean', 'max', 'min'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Give me descriptive statistics for the entire data frame**" ] }, { "cell_type": "code", "execution_count": 18, "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", "
CustomerIDQuantityUnitPrice
count10729.00000015000.00000015000.000000
mean15590.7766807.4640004.103233
std1764.02516085.92438720.104873
min12347.000000-9360.0000000.000000
25%14227.9348451.0000001.250000
50%15669.1382352.0000002.510000
75%17212.6900926.6102624.211297
max18239.0000002880.000000950.990000
\n", "
" ], "text/plain": [ " CustomerID Quantity UnitPrice\n", "count 10729.000000 15000.000000 15000.000000\n", "mean 15590.776680 7.464000 4.103233\n", "std 1764.025160 85.924387 20.104873\n", "min 12347.000000 -9360.000000 0.000000\n", "25% 14227.934845 1.000000 1.250000\n", "50% 15669.138235 2.000000 2.510000\n", "75% 17212.690092 6.610262 4.211297\n", "max 18239.000000 2880.000000 950.990000" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Show me a histogram of numeric columns**" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df[(df['Quantity']>-50) & \n", " (df['Quantity']<50) & \n", " (df['UnitPrice']>0) & \n", " (df['UnitPrice']<100)][['Quantity', 'UnitPrice']].hist(figsize=[12,4], bins=30)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df[(df['Quantity']>-50) & \n", " (df['Quantity']<50) & \n", " (df['UnitPrice']>0) & \n", " (df['UnitPrice']<100)][['Quantity', 'UnitPrice']].hist(figsize=[12,4], bins=30, log=True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryCustomerID...StockCodeUnitPrice
1228United Kingdom15485.0...220862.55
1237Norway12433.0...224441.06
1286Norway12433.0...840501.25
1293Norway12433.0...221970.85
1333United Kingdom18144.0...848791.69
..................
14784United Kingdom15061.0...2242310.95
14785United Kingdom15061.0...220751.45
14788United Kingdom15061.0...170380.07
14974United Kingdom14739.0...217040.72
14980United Kingdom14739.0...221781.06
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258 rows × 8 columns

" ], "text/plain": [ " Country CustomerID ... StockCode UnitPrice\n", "1228 United Kingdom 15485.0 ... 22086 2.55\n", "1237 Norway 12433.0 ... 22444 1.06\n", "1286 Norway 12433.0 ... 84050 1.25\n", "1293 Norway 12433.0 ... 22197 0.85\n", "1333 United Kingdom 18144.0 ... 84879 1.69\n", "... ... ... ... ... ...\n", "14784 United Kingdom 15061.0 ... 22423 10.95\n", "14785 United Kingdom 15061.0 ... 22075 1.45\n", "14788 United Kingdom 15061.0 ... 17038 0.07\n", "14974 United Kingdom 14739.0 ... 21704 0.72\n", "14980 United Kingdom 14739.0 ... 22178 1.06\n", "\n", "[258 rows x 8 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.query('Quantity>50 & UnitPrice<100')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Arithmetic Operations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Numeric values" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1000 1\n", "1001 1\n", "1002 1\n", "1003 1\n", "1004 12\n", "Name: Quantity, dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Quantity'].head()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1000 1.25\n", "1001 1.25\n", "1002 1.25\n", "1003 1.25\n", "1004 0.29\n", "Name: UnitPrice, dtype: float64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['UnitPrice'].head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "product = df['Quantity'] * df['UnitPrice']" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1000 1.25\n", "1001 1.25\n", "1002 1.25\n", "1003 1.25\n", "1004 3.48\n", "dtype: float64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "product.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "String concatenation" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1000 United Kingdom21123\n", "1001 United Kingdom21124\n", "1002 United Kingdom21122\n", "1003 United Kingdom84378\n", "1004 United Kingdom21985\n", " ... \n", "14995 United Kingdom72349B\n", "14996 United Kingdom72741\n", "14997 United Kingdom22762\n", "14998 United Kingdom21773\n", "14999 United Kingdom22149\n", "Length: 15000, dtype: object" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Country'] + df['StockCode']" ] } ], "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": "3.7.5" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 2 }