| { |
| "cells": [ |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# People Data Labs + Hamilton\n", |
| "This notebook will teach you how to use People Data Labs (PDL) [Company enrichment](https://docs.peopledatalabs.com/docs/company-enrichment-api) data along stock market data for financial analysis. We will introduce the Python library [Hamilon](https://hamilton.dagworks.io/en/latest/?badge=latest) to help create data transformations.\n", |
| "\n", |
| "**Content**\n", |
| "1. Data preparation\n", |
| "2. Analytics: Explore the relationship between employee count and stock growth" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## 0. Imports\n", |
| "Make sure you followed the `README` to install dependencies and download the data." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 1, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "import pandas as pd\n", |
| "from hamilton import driver\n", |
| "from IPython.display import display\n", |
| "\n", |
| "# Loads a \"jupyter magic\" that allows special notebook interactions\n", |
| "%load_ext hamilton.plugins.jupyter_magic" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## 1. Load raw data\n", |
| "Hamilton [uses Python functions to define a dataflow](https://hamilton.dagworks.io/en/latest/concepts/node/) of transformations. \n", |
| "\n", |
| "The next cell starts with the special statement `%%cell_to_module` and includes Python functions to define steps of our analysis. \n", |
| "\n", |
| "Executing the cell will produce a visualization of the flow of operations." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "data": { |
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| "<title>pdl_data->employee_count_by_month_df</title>\n", |
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| "<g id=\"edge2\" class=\"edge\">\n", |
| "<title>pdl_data->company_info</title>\n", |
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| "<title>stock_data</title>\n", |
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| "<title>_pdl_data_inputs->pdl_data</title>\n", |
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| "<title>function</title>\n", |
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| } |
| ], |
| "source": [ |
| "%%cell_to_module -m data_preparation -d\n", |
| "\n", |
| "from pathlib import Path\n", |
| "import pandas as pd\n", |
| "\n", |
| "\n", |
| "def pdl_data(pdl_file: str, data_dir: str = \"data/\") -> pd.DataFrame:\n", |
| " \"\"\"Load raw Pêople Data Labs data stored locally\"\"\"\n", |
| " return pd.read_json(Path(data_dir, pdl_file))\n", |
| "\n", |
| "\n", |
| "def stock_data(stock_file: str, data_dir: str = \"data/\") -> pd.DataFrame:\n", |
| " \"\"\"Load raw stock data stored locally\"\"\"\n", |
| " return pd.read_json(Path(data_dir, stock_file))\n", |
| "\n", |
| "\n", |
| "def company_info(pdl_data: pd.DataFrame) -> pd.DataFrame:\n", |
| " \"\"\"Select columns containing general company info\"\"\"\n", |
| " columns = [\n", |
| " \"id\", \"ticker\", \"website\", \"name\", \"display_name\", \"legal_name\", \"founded\", \n", |
| " \"industry\", \"type\", \"summary\", \"total_funding_raised\", \"latest_funding_stage\",\n", |
| " \"number_funding_rounds\", \"last_funding_date\", \"inferred_revenue\"\n", |
| " ]\n", |
| " return pdl_data[columns]\n", |
| "\n", |
| "\n", |
| "def employee_count_by_month_df(pdl_data: pd.DataFrame) -> pd.DataFrame:\n", |
| " \"\"\"Normalized employee count data\"\"\"\n", |
| " return (\n", |
| " pd.json_normalize(pdl_data[\"employee_count_by_month\"])\n", |
| " .assign(ticker=pdl_data[\"ticker\"])\n", |
| " .melt(\n", |
| " id_vars=\"ticker\",\n", |
| " var_name=\"year_month\",\n", |
| " value_name=\"employee_count\",\n", |
| " )\n", |
| " .astype({\"year_month\": \"datetime64[ns]\"})\n", |
| " )" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "### Execute your first dataflow\n", |
| "This first cell creates a Hamilton `Driver` used to execute code. \n", |
| "\n", |
| "We pass it the `data_preparation` module define in the cell above (with `%%cell_to_module -m data_preparation`)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 3, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "hamilton_driver = (\n", |
| " driver.Builder()\n", |
| " .with_modules(data_preparation)\n", |
| " .build()\n", |
| ")" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "We specify the input values and the variables we want to compute.\n", |
| "\n", |
| "In this case, we pass the path to the `pdl_file` and request `company_info`." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 4, |
| "metadata": {}, |
| "outputs": [ |
| { |
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| "<div>\n", |
| "<style scoped>\n", |
| " .dataframe tbody tr th:only-of-type {\n", |
| " vertical-align: middle;\n", |
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| "\n", |
| " .dataframe tbody tr th {\n", |
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| "<table border=\"1\" class=\"dataframe\">\n", |
| " <thead>\n", |
| " <tr style=\"text-align: right;\">\n", |
| " <th></th>\n", |
| " <th>id</th>\n", |
| " <th>ticker</th>\n", |
| " <th>website</th>\n", |
| " <th>name</th>\n", |
| " <th>display_name</th>\n", |
| " <th>legal_name</th>\n", |
| " <th>founded</th>\n", |
| " <th>industry</th>\n", |
| " <th>type</th>\n", |
| " <th>summary</th>\n", |
| " <th>total_funding_raised</th>\n", |
| " <th>latest_funding_stage</th>\n", |
| " <th>number_funding_rounds</th>\n", |
| " <th>last_funding_date</th>\n", |
| " <th>inferred_revenue</th>\n", |
| " </tr>\n", |
| " </thead>\n", |
| " <tbody>\n", |
| " <tr>\n", |
| " <th>0</th>\n", |
| " <td>firstbankpr</td>\n", |
| " <td>FBP</td>\n", |
| " <td>1firstbank.com</td>\n", |
| " <td>firstbank</td>\n", |
| " <td>FirstBank</td>\n", |
| " <td>FIRST BANCORP</td>\n", |
| " <td>1948.0</td>\n", |
| " <td>banking</td>\n", |
| " <td>public</td>\n", |
| " <td>backed by a history spanning over 70 years, fi...</td>\n", |
| " <td>NaN</td>\n", |
| " <td>None</td>\n", |
| " <td>NaN</td>\n", |
| " <td>None</td>\n", |
| " <td>$100M-$250M</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>1</th>\n", |
| " <td>motorolasolutions</td>\n", |
| " <td>MSI</td>\n", |
| " <td>motorolasolutions.com</td>\n", |
| " <td>motorola solutions</td>\n", |
| " <td>Motorola Solutions</td>\n", |
| " <td>Motorola Solutions, Inc.</td>\n", |
| " <td>1928.0</td>\n", |
| " <td>telecommunications</td>\n", |
| " <td>public</td>\n", |
| " <td>motorola solutions is a global leader in publi...</td>\n", |
| " <td>1.000000e+09</td>\n", |
| " <td>post_ipo_equity</td>\n", |
| " <td>1.0</td>\n", |
| " <td>2023-01-06</td>\n", |
| " <td>$10B+</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>2</th>\n", |
| " <td>american-equity</td>\n", |
| " <td>AEL-PA</td>\n", |
| " <td>american-equity.com</td>\n", |
| " <td>american equity</td>\n", |
| " <td>American Equity</td>\n", |
| " <td>None</td>\n", |
| " <td>1995.0</td>\n", |
| " <td>insurance</td>\n", |
| " <td>public</td>\n", |
| " <td>american equity investment life insurance comp...</td>\n", |
| " <td>2.530000e+08</td>\n", |
| " <td>post_ipo_equity</td>\n", |
| " <td>2.0</td>\n", |
| " <td>2022-01-07</td>\n", |
| " <td>$250M-$500M</td>\n", |
| " </tr>\n", |
| " </tbody>\n", |
| "</table>\n", |
| "</div>" |
| ], |
| "text/plain": [ |
| " id ticker website name \\\n", |
| "0 firstbankpr FBP 1firstbank.com firstbank \n", |
| "1 motorolasolutions MSI motorolasolutions.com motorola solutions \n", |
| "2 american-equity AEL-PA american-equity.com american equity \n", |
| "\n", |
| " display_name legal_name founded industry \\\n", |
| "0 FirstBank FIRST BANCORP 1948.0 banking \n", |
| "1 Motorola Solutions Motorola Solutions, Inc. 1928.0 telecommunications \n", |
| "2 American Equity None 1995.0 insurance \n", |
| "\n", |
| " type summary \\\n", |
| "0 public backed by a history spanning over 70 years, fi... \n", |
| "1 public motorola solutions is a global leader in publi... \n", |
| "2 public american equity investment life insurance comp... \n", |
| "\n", |
| " total_funding_raised latest_funding_stage number_funding_rounds \\\n", |
| "0 NaN None NaN \n", |
| "1 1.000000e+09 post_ipo_equity 1.0 \n", |
| "2 2.530000e+08 post_ipo_equity 2.0 \n", |
| "\n", |
| " last_funding_date inferred_revenue \n", |
| "0 None $100M-$250M \n", |
| "1 2023-01-06 $10B+ \n", |
| "2 2022-01-07 $250M-$500M " |
| ] |
| }, |
| "metadata": {}, |
| "output_type": "display_data" |
| } |
| ], |
| "source": [ |
| "inputs = dict(pdl_file=\"pdl_data.json\")\n", |
| "\n", |
| "results = hamilton_driver.execute([\"company_info\"], inputs=inputs)\n", |
| "\n", |
| "# `display()` can \"print\" multiple values for a single cell\n", |
| "# display the execution path and the result for `company_info`\n", |
| "display(\n", |
| " hamilton_driver.visualize_execution([\"company_info\"], inputs=inputs),\n", |
| " results[\"company_info\"].head(3)\n", |
| ")" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## 2. Analytics\n", |
| "We're interested in the potential relationship between employee count and stock growth in private companies (series A to D). \n", |
| "\n", |
| "We will need to:\n", |
| "- filter companies by funding stage\n", |
| "- define a window period since last funding round\n", |
| "- compute growth for employee count and stock\n", |
| "\n", |
| "NOTE. We need to mention imports in each cell with `%%cell_to_module` (e.g., `import pandas as pd`) even if a package was imported previously." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 5, |
| "metadata": {}, |
| "outputs": [ |
| { |
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| }, |
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| "metadata": {}, |
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| } |
| ], |
| "source": [ |
| "%%cell_to_module -m analytics -d\n", |
| "\n", |
| "import pandas as pd\n", |
| "\n", |
| "\n", |
| "def n_company_by_funding_stage(company_info: pd.DataFrame) -> pd.DataFrame:\n", |
| " \"\"\"Get the number of company per funding stage\"\"\"\n", |
| " return (\n", |
| " company_info\n", |
| " .groupby(\"latest_funding_stage\")[\"latest_funding_stage\"]\n", |
| " .value_counts()\n", |
| " .sort_values(ascending=False)\n", |
| " )\n", |
| "\n", |
| "\n", |
| "def selected_companies(company_info: pd.DataFrame, rounds_selection: list[str]) -> pd.DataFrame:\n", |
| " \"\"\"Companies with `latest_funding_stage` included in `rounds_selection\"\"\"\n", |
| " return company_info.loc[company_info.latest_funding_stage.isin(rounds_selection)]\n", |
| "\n", |
| " \n", |
| "def employees_since_last_funding_round(\n", |
| " employee_count_by_month_df: pd.DataFrame,\n", |
| " selected_companies: pd.DataFrame,\n", |
| ") -> pd.DataFrame:\n", |
| " \"\"\"Select employee count data since the last funding round\"\"\"\n", |
| " employee_count_by_month_df = employee_count_by_month_df.loc[\n", |
| " employee_count_by_month_df.ticker.isin(selected_companies.ticker)\n", |
| " ]\n", |
| " df = pd.merge(\n", |
| " left=employee_count_by_month_df,\n", |
| " right=selected_companies[[\"ticker\", \"last_funding_date\"]],\n", |
| " on=\"ticker\",\n", |
| " how=\"left\"\n", |
| " )\n", |
| " return df.loc[df.year_month > df.last_funding_date]\n", |
| "\n", |
| "\n", |
| "def _growth_rate(group):\n", |
| " \"\"\"aggregation for growth rate; data needs to be sorted\"\"\"\n", |
| " return (group.iloc[-1] - group.iloc[0]) / group.iloc[0]\n", |
| "\n", |
| "\n", |
| "def employee_growth_rate_since_last_funding_round(\n", |
| " employees_since_last_funding_round: pd.DataFrame,\n", |
| ") -> pd.DataFrame:\n", |
| " \"\"\"Employee count growth rate since last funding round\"\"\" \n", |
| " return (\n", |
| " employees_since_last_funding_round\n", |
| " .sort_values(by=\"year_month\", ascending=True)\n", |
| " .groupby(\"ticker\")[\"employee_count\"]\n", |
| " .aggregate(_growth_rate)\n", |
| " .sort_values(ascending=False)\n", |
| " .reset_index()\n", |
| " .rename(columns={\"employee_count\": \"employee_growth\"})\n", |
| " )\n", |
| " \n", |
| " \n", |
| "def stock_growth_rate_since_last_funding_round(\n", |
| " stock_data: pd.DataFrame,\n", |
| " employees_since_last_funding_round: pd.DataFrame,\n", |
| ") -> pd.DataFrame:\n", |
| " \"\"\"Stock data since last funding round.\n", |
| " Returns None is no stock history or window found.\n", |
| " \n", |
| " NOTE. We use the minimum date from the employee count history instead of the true\n", |
| " funding round date to ensure growth rates cover the same period\n", |
| " \"\"\"\n", |
| " period_start = (\n", |
| " employees_since_last_funding_round\n", |
| " .groupby(\"ticker\")[\"year_month\"]\n", |
| " .min()\n", |
| " .reset_index()\n", |
| " )\n", |
| " df = pd.merge(left=stock_data, right=period_start, on=\"ticker\", how=\"inner\")\n", |
| "\n", |
| " stock_growth = dict()\n", |
| " for idx, row in df.iterrows():\n", |
| " history = pd.json_normalize(row[\"historical_price\"]).astype({\"date\": \"datetime64[ns]\"})\n", |
| " \n", |
| " # skip ticker if history is empty\n", |
| " if history.empty:\n", |
| " stock_growth[row.ticker] = None\n", |
| " continue\n", |
| " \n", |
| " window = history[history.date > row.year_month]\n", |
| " \n", |
| " # skip ticker if window is empty\n", |
| " if window.empty:\n", |
| " stock_growth[row.ticker] = None\n", |
| " continue\n", |
| " \n", |
| " stock_growth[row.ticker] = _growth_rate(window[\"close\"])\n", |
| " \n", |
| " return (\n", |
| " pd.DataFrame()\n", |
| " .from_dict(stock_growth, orient=\"index\")\n", |
| " .reset_index()\n", |
| " .rename(columns={\"index\": \"ticker\", 0: \"stock_growth\"})\n", |
| " )\n", |
| " \n", |
| " \n", |
| "def augmented_company_info(\n", |
| " selected_companies: pd.DataFrame,\n", |
| " employee_growth_rate_since_last_funding_round: pd.DataFrame,\n", |
| " stock_growth_rate_since_last_funding_round: pd.DataFrame,\n", |
| ") -> pd.DataFrame:\n", |
| " \"\"\"Merge employee count and stock growth with company info\"\"\"\n", |
| " df = pd.merge(\n", |
| " selected_companies,\n", |
| " employee_growth_rate_since_last_funding_round,\n", |
| " on=\"ticker\",\n", |
| " how=\"left\",\n", |
| " )\n", |
| " df = pd.merge(\n", |
| " df,\n", |
| " stock_growth_rate_since_last_funding_round,\n", |
| " on=\"ticker\",\n", |
| " how=\"left\",\n", |
| " )\n", |
| " return df" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "### Execute the dataflow\n", |
| "We create another Driver to include the newly defined `analytics` module. \n", |
| "\n", |
| "Notice that the `Builder().with_modules()` can receive more than one module. Indeed, the visualization includes nodes both `data_preparation` and `analytics`." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 6, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "data": { |
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| "<text text-anchor=\"start\" x=\"324.5\" y=\"-173.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">rounds_selection</text>\n", |
| "<text text-anchor=\"start\" x=\"447.5\" y=\"-173.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">list</text>\n", |
| "</g>\n", |
| "<!-- _selected_companies_inputs->selected_companies -->\n", |
| "<g id=\"edge5\" class=\"edge\">\n", |
| "<title>_selected_companies_inputs->selected_companies</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M483.57,-177.08C513.52,-176.76 547.36,-176.4 578.32,-176.07\"/>\n", |
| "<polygon fill=\"black\" stroke=\"black\" points=\"578.74,-179.56 588.7,-175.96 578.66,-172.56 578.74,-179.56\"/>\n", |
| "</g>\n", |
| "<!-- _pdl_data_inputs -->\n", |
| "<g id=\"node12\" class=\"node\">\n", |
| "<title>_pdl_data_inputs</title>\n", |
| "<polygon fill=\"#ffffff\" stroke=\"black\" stroke-dasharray=\"5,2\" points=\"112,-211 0,-211 0,-145 112,-145 112,-211\"/>\n", |
| "<text text-anchor=\"start\" x=\"18.5\" y=\"-184.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">pdl_file</text>\n", |
| "<text text-anchor=\"start\" x=\"78\" y=\"-184.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">str</text>\n", |
| "<text text-anchor=\"start\" x=\"15\" y=\"-163.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">data_dir</text>\n", |
| "<text text-anchor=\"start\" x=\"78\" y=\"-163.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">str</text>\n", |
| "</g>\n", |
| "<!-- _pdl_data_inputs->pdl_data -->\n", |
| "<g id=\"edge9\" class=\"edge\">\n", |
| "<title>_pdl_data_inputs->pdl_data</title>\n", |
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| "<polygon fill=\"black\" stroke=\"black\" points=\"130.76,-181.5 140.76,-178 130.76,-174.5 130.76,-181.5\"/>\n", |
| "</g>\n", |
| "<!-- _stock_data_inputs -->\n", |
| "<g id=\"node13\" class=\"node\">\n", |
| "<title>_stock_data_inputs</title>\n", |
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| "<text text-anchor=\"start\" x=\"638\" y=\"-39.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">data_dir</text>\n", |
| "<text text-anchor=\"start\" x=\"705\" y=\"-39.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">str</text>\n", |
| "<text text-anchor=\"start\" x=\"634\" y=\"-18.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">stock_file</text>\n", |
| "<text text-anchor=\"start\" x=\"705\" y=\"-18.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">str</text>\n", |
| "</g>\n", |
| "<!-- _stock_data_inputs->stock_data -->\n", |
| "<g id=\"edge10\" class=\"edge\">\n", |
| "<title>_stock_data_inputs->stock_data</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M739.29,-33C792.49,-33 870.34,-33 924.61,-33\"/>\n", |
| "<polygon fill=\"black\" stroke=\"black\" points=\"924.8,-36.5 934.8,-33 924.8,-29.5 924.8,-36.5\"/>\n", |
| "</g>\n", |
| "<!-- input -->\n", |
| "<g id=\"node14\" class=\"node\">\n", |
| "<title>input</title>\n", |
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| "</g>\n", |
| "<!-- function -->\n", |
| "<g id=\"node15\" class=\"node\">\n", |
| "<title>function</title>\n", |
| "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M84,-266.5C84,-266.5 28,-266.5 28,-266.5 22,-266.5 16,-260.5 16,-254.5 16,-254.5 16,-241.5 16,-241.5 16,-235.5 22,-229.5 28,-229.5 28,-229.5 84,-229.5 84,-229.5 90,-229.5 96,-235.5 96,-241.5 96,-241.5 96,-254.5 96,-254.5 96,-260.5 90,-266.5 84,-266.5\"/>\n", |
| "<text text-anchor=\"middle\" x=\"56\" y=\"-244.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">function</text>\n", |
| "</g>\n", |
| "</g>\n", |
| "</svg>\n" |
| ], |
| "text/plain": [ |
| "<hamilton.driver.Driver at 0x7f6a0a5e78e0>" |
| ] |
| }, |
| "execution_count": 6, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "analytics_driver = (\n", |
| " driver.Builder()\n", |
| " .with_modules(data_preparation, analytics)\n", |
| " .build()\n", |
| ")\n", |
| "analytics_driver" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 7, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "data": { |
| "image/svg+xml": [ |
| "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", |
| "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", |
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| " -->\n", |
| "<!-- Title: %3 Pages: 1 -->\n", |
| "<svg width=\"1835pt\" height=\"412pt\"\n", |
| " viewBox=\"0.00 0.00 1834.50 412.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", |
| "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 408)\">\n", |
| "<title>%3</title>\n", |
| "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-408 1830.5,-408 1830.5,4 -4,4\"/>\n", |
| "<g id=\"clust1\" class=\"cluster\">\n", |
| "<title>cluster__legend</title>\n", |
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| "<text text-anchor=\"middle\" x=\"56\" y=\"-380.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Legend</text>\n", |
| "</g>\n", |
| "<!-- company_info -->\n", |
| "<g id=\"node1\" class=\"node\">\n", |
| "<title>company_info</title>\n", |
| "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M446,-282C446,-282 340,-282 340,-282 334,-282 328,-276 328,-270 328,-270 328,-230 328,-230 328,-224 334,-218 340,-218 340,-218 446,-218 446,-218 452,-218 458,-224 458,-230 458,-230 458,-270 458,-270 458,-276 452,-282 446,-282\"/>\n", |
| "<text text-anchor=\"start\" x=\"339\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">company_info</text>\n", |
| "<text text-anchor=\"start\" x=\"354.5\" y=\"-232.8\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">DataFrame</text>\n", |
| "</g>\n", |
| "<!-- selected_companies -->\n", |
| "<g id=\"node4\" class=\"node\">\n", |
| "<title>selected_companies</title>\n", |
| "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M753.5,-206C753.5,-206 597.5,-206 597.5,-206 591.5,-206 585.5,-200 585.5,-194 585.5,-194 585.5,-154 585.5,-154 585.5,-148 591.5,-142 597.5,-142 597.5,-142 753.5,-142 753.5,-142 759.5,-142 765.5,-148 765.5,-154 765.5,-154 765.5,-194 765.5,-194 765.5,-200 759.5,-206 753.5,-206\"/>\n", |
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| "</g>\n", |
| "<!-- company_info->selected_companies -->\n", |
| "<g id=\"edge4\" class=\"edge\">\n", |
| "<title>company_info->selected_companies</title>\n", |
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| "<polygon fill=\"black\" stroke=\"black\" points=\"576.53,-204.17 585.26,-198.18 574.7,-197.42 576.53,-204.17\"/>\n", |
| "</g>\n", |
| "<!-- n_company_by_funding_stage -->\n", |
| "<g id=\"node5\" class=\"node\">\n", |
| "<title>n_company_by_funding_stage</title>\n", |
| "<path fill=\"#ffc857\" stroke=\"black\" d=\"M789.5,-288C789.5,-288 561.5,-288 561.5,-288 555.5,-288 549.5,-282 549.5,-276 549.5,-276 549.5,-236 549.5,-236 549.5,-230 555.5,-224 561.5,-224 561.5,-224 789.5,-224 789.5,-224 795.5,-224 801.5,-230 801.5,-236 801.5,-236 801.5,-276 801.5,-276 801.5,-282 795.5,-288 789.5,-288\"/>\n", |
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| "</g>\n", |
| "<!-- company_info->n_company_by_funding_stage -->\n", |
| "<g id=\"edge6\" class=\"edge\">\n", |
| "<title>company_info->n_company_by_funding_stage</title>\n", |
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| "<!-- employee_growth_rate_since_last_funding_round -->\n", |
| "<g id=\"node2\" class=\"node\">\n", |
| "<title>employee_growth_rate_since_last_funding_round</title>\n", |
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| "</g>\n", |
| "<!-- augmented_company_info -->\n", |
| "<g id=\"node10\" class=\"node\">\n", |
| "<title>augmented_company_info</title>\n", |
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| "<title>employee_growth_rate_since_last_funding_round->augmented_company_info</title>\n", |
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| "<title>employee_count_by_month_df</title>\n", |
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| "<title>employees_since_last_funding_round</title>\n", |
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| "<g id=\"edge7\" class=\"edge\">\n", |
| "<title>employee_count_by_month_df->employees_since_last_funding_round</title>\n", |
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| "<g id=\"edge8\" class=\"edge\">\n", |
| "<title>selected_companies->employees_since_last_funding_round</title>\n", |
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| "<title>selected_companies->augmented_company_info</title>\n", |
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| "<g id=\"edge2\" class=\"edge\">\n", |
| "<title>employees_since_last_funding_round->employee_growth_rate_since_last_funding_round</title>\n", |
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| "<title>stock_growth_rate_since_last_funding_round</title>\n", |
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| "<title>employees_since_last_funding_round->stock_growth_rate_since_last_funding_round</title>\n", |
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| "<title>stock_data</title>\n", |
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| "<title>stock_data->stock_growth_rate_since_last_funding_round</title>\n", |
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| "<g id=\"node8\" class=\"node\">\n", |
| "<title>pdl_data</title>\n", |
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| "<!-- pdl_data->company_info -->\n", |
| "<g id=\"edge1\" class=\"edge\">\n", |
| "<title>pdl_data->company_info</title>\n", |
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| "<g id=\"edge3\" class=\"edge\">\n", |
| "<title>pdl_data->employee_count_by_month_df</title>\n", |
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| "<title>_stock_data_inputs->stock_data</title>\n", |
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| "<div>\n", |
| "<style scoped>\n", |
| " .dataframe tbody tr th:only-of-type {\n", |
| " vertical-align: middle;\n", |
| " }\n", |
| "\n", |
| " .dataframe tbody tr th {\n", |
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| "\n", |
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| " text-align: right;\n", |
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| "</style>\n", |
| "<table border=\"1\" class=\"dataframe\">\n", |
| " <thead>\n", |
| " <tr style=\"text-align: right;\">\n", |
| " <th></th>\n", |
| " <th>id</th>\n", |
| " <th>ticker</th>\n", |
| " <th>website</th>\n", |
| " <th>name</th>\n", |
| " <th>display_name</th>\n", |
| " <th>legal_name</th>\n", |
| " <th>founded</th>\n", |
| " <th>industry</th>\n", |
| " <th>type</th>\n", |
| " <th>summary</th>\n", |
| " <th>total_funding_raised</th>\n", |
| " <th>latest_funding_stage</th>\n", |
| " <th>number_funding_rounds</th>\n", |
| " <th>last_funding_date</th>\n", |
| " <th>inferred_revenue</th>\n", |
| " <th>employee_growth</th>\n", |
| " <th>stock_growth</th>\n", |
| " </tr>\n", |
| " </thead>\n", |
| " <tbody>\n", |
| " <tr>\n", |
| " <th>0</th>\n", |
| " <td>relxtech</td>\n", |
| " <td>RLX</td>\n", |
| " <td>relxtech.com</td>\n", |
| " <td>relx technology</td>\n", |
| " <td>Relx Technology</td>\n", |
| " <td>None</td>\n", |
| " <td>2018.0</td>\n", |
| " <td>consumer electronics</td>\n", |
| " <td>public</td>\n", |
| " <td>-</td>\n", |
| " <td>5.755918e+06</td>\n", |
| " <td>series_c</td>\n", |
| " <td>4.0</td>\n", |
| " <td>2019-08-15</td>\n", |
| " <td>$50M-$100M</td>\n", |
| " <td>0.552632</td>\n", |
| " <td>14.133333</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>1</th>\n", |
| " <td>beike</td>\n", |
| " <td>BEKE</td>\n", |
| " <td>ke.com</td>\n", |
| " <td>贝壳找房ke.com</td>\n", |
| " <td>贝壳找房ke.com</td>\n", |
| " <td>None</td>\n", |
| " <td>2018.0</td>\n", |
| " <td>real estate</td>\n", |
| " <td>public</td>\n", |
| " <td>beiker is a technology-driven new housing serv...</td>\n", |
| " <td>3.602538e+09</td>\n", |
| " <td>series_d</td>\n", |
| " <td>6.0</td>\n", |
| " <td>2020-03-05</td>\n", |
| " <td>$250M-$500M</td>\n", |
| " <td>0.112971</td>\n", |
| " <td>1.653437</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>2</th>\n", |
| " <td>upstart-network</td>\n", |
| " <td>UPST</td>\n", |
| " <td>upstart.com</td>\n", |
| " <td>upstart</td>\n", |
| " <td>Upstart</td>\n", |
| " <td>None</td>\n", |
| " <td>2012.0</td>\n", |
| " <td>financial services</td>\n", |
| " <td>public</td>\n", |
| " <td>founded by ex-googlers, upstart is the first l...</td>\n", |
| " <td>1.440500e+08</td>\n", |
| " <td>series_d</td>\n", |
| " <td>7.0</td>\n", |
| " <td>2019-04-08</td>\n", |
| " <td>$500M-$1B</td>\n", |
| " <td>3.952218</td>\n", |
| " <td>0.220795</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>3</th>\n", |
| " <td>alarm-com</td>\n", |
| " <td>ALRM</td>\n", |
| " <td>alarm.com</td>\n", |
| " <td>alarm.com</td>\n", |
| " <td>Alarm.com</td>\n", |
| " <td>None</td>\n", |
| " <td>2000.0</td>\n", |
| " <td>information technology and services</td>\n", |
| " <td>public</td>\n", |
| " <td>alarm.com is the leading platform for the inte...</td>\n", |
| " <td>1.630000e+08</td>\n", |
| " <td>series_b</td>\n", |
| " <td>2.0</td>\n", |
| " <td>2012-07-24</td>\n", |
| " <td>$250M-$500M</td>\n", |
| " <td>7.906040</td>\n", |
| " <td>-0.154480</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>4</th>\n", |
| " <td>51talkhq</td>\n", |
| " <td>COE</td>\n", |
| " <td>51talk.com</td>\n", |
| " <td>51talk hq</td>\n", |
| " <td>51Talk HQ</td>\n", |
| " <td>None</td>\n", |
| " <td>2011.0</td>\n", |
| " <td>internet</td>\n", |
| " <td>public</td>\n", |
| " <td>founded in 2011, headquartered in singapore, 5...</td>\n", |
| " <td>6.712601e+07</td>\n", |
| " <td>series_c</td>\n", |
| " <td>5.0</td>\n", |
| " <td>2014-10-23</td>\n", |
| " <td>$100M-$250M</td>\n", |
| " <td>3.103896</td>\n", |
| " <td>2.714286</td>\n", |
| " </tr>\n", |
| " </tbody>\n", |
| "</table>\n", |
| "</div>" |
| ], |
| "text/plain": [ |
| " id ticker website name display_name \\\n", |
| "0 relxtech RLX relxtech.com relx technology Relx Technology \n", |
| "1 beike BEKE ke.com 贝壳找房ke.com 贝壳找房ke.com \n", |
| "2 upstart-network UPST upstart.com upstart Upstart \n", |
| "3 alarm-com ALRM alarm.com alarm.com Alarm.com \n", |
| "4 51talkhq COE 51talk.com 51talk hq 51Talk HQ \n", |
| "\n", |
| " legal_name founded industry type \\\n", |
| "0 None 2018.0 consumer electronics public \n", |
| "1 None 2018.0 real estate public \n", |
| "2 None 2012.0 financial services public \n", |
| "3 None 2000.0 information technology and services public \n", |
| "4 None 2011.0 internet public \n", |
| "\n", |
| " summary total_funding_raised \\\n", |
| "0 - 5.755918e+06 \n", |
| "1 beiker is a technology-driven new housing serv... 3.602538e+09 \n", |
| "2 founded by ex-googlers, upstart is the first l... 1.440500e+08 \n", |
| "3 alarm.com is the leading platform for the inte... 1.630000e+08 \n", |
| "4 founded in 2011, headquartered in singapore, 5... 6.712601e+07 \n", |
| "\n", |
| " latest_funding_stage number_funding_rounds last_funding_date \\\n", |
| "0 series_c 4.0 2019-08-15 \n", |
| "1 series_d 6.0 2020-03-05 \n", |
| "2 series_d 7.0 2019-04-08 \n", |
| "3 series_b 2.0 2012-07-24 \n", |
| "4 series_c 5.0 2014-10-23 \n", |
| "\n", |
| " inferred_revenue employee_growth stock_growth \n", |
| "0 $50M-$100M 0.552632 14.133333 \n", |
| "1 $250M-$500M 0.112971 1.653437 \n", |
| "2 $500M-$1B 3.952218 0.220795 \n", |
| "3 $250M-$500M 7.906040 -0.154480 \n", |
| "4 $100M-$250M 3.103896 2.714286 " |
| ] |
| }, |
| "metadata": {}, |
| "output_type": "display_data" |
| } |
| ], |
| "source": [ |
| "inputs = dict(\n", |
| " pdl_file=\"pdl_data.json\",\n", |
| " stock_file=\"stock_data.json\",\n", |
| " rounds_selection=[\"series_a\", \"series_b\", \"series_c\", \"series_d\"]\n", |
| ")\n", |
| "\n", |
| "final_vars = [\n", |
| " \"n_company_by_funding_stage\",\n", |
| " \"augmented_company_info\",\n", |
| "]\n", |
| "\n", |
| "results = analytics_driver.execute(final_vars, inputs=inputs)\n", |
| "\n", |
| "display(\n", |
| " analytics_driver.visualize_execution(final_vars, inputs=inputs),\n", |
| " results[\"augmented_company_info\"].head(),\n", |
| ")" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# Conclusion\n", |
| "Congrats! You concluded the introduction to People Data Labs + Hamilton!\n", |
| "\n", |
| "You now know the basics of Hamilton and how it can help you define data transformations. If you haven't, visit the README of the repository to learn how to use Hamilton outside of notebooks." |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "### Resources\n", |
| "- [PDL Blog](https://blog.peopledatalabs.com/) and [PDL Recipes](https://docs.peopledatalabs.com/recipes)\n", |
| "- [Interactive Hamilton training](https://www.tryhamilton.dev/hamilton-basics/jumping-in)\n", |
| "- [Hamilton documentation](https://hamilton.dagworks.io/en/latest/concepts/node/)\n", |
| "- more [Hamilton code examples](https://github.com/DAGWorks-Inc/hamilton/tree/main/examples) and integrations with Python tools." |
| ] |
| } |
| ], |
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| "name": "python", |
| "nbconvert_exporter": "python", |
| "pygments_lexer": "ipython3", |
| "version": "3.10.9" |
| } |
| }, |
| "nbformat": 4, |
| "nbformat_minor": 2 |
| } |
