{"id":479,"date":"2022-08-16T08:40:32","date_gmt":"2022-08-16T08:40:32","guid":{"rendered":"https:\/\/eodhd.com\/financial-academy\/?p=479"},"modified":"2025-02-05T12:32:24","modified_gmt":"2025-02-05T12:32:24","slug":"filtering-and-visualizing-the-stocks-data-analysis-results-in-python","status":"publish","type":"post","link":"https:\/\/eodhd.com\/financial-academy\/stocks-data-analysis-examples\/filtering-and-visualizing-the-stocks-data-analysis-results-in-python","title":{"rendered":"Filtering and visualizing the Stocks Data Analysis results in Python"},"content":{"rendered":"\n<p id=\"block-4d4cab9f-88f6-401c-b750-5ee99586ce28\"><strong>Preface: <\/strong>In this video series Matt shows how to use the EODHD Financial APIs for the Stock Analysis with Python.<\/p>\n\n\n\n<p id=\"block-7930bab7-bffc-4a86-90ab-c44fe5e58e52\"><strong>Don&#8217;t forget to explore other 2 Parts to learn more:<\/strong><br>Part 1 <a href=\"https:\/\/eodhistoricaldata.com\/financial-academy\/get-financial-data-samples\/obtaining-stocks-data-for-the-analysis-in-python\/\">Obtaining Stocks Data for the Analysis in Python<\/a><br>Part 2 <a href=\"https:\/\/eodhistoricaldata.com\/financial-academy\/data-processing-samples-and-manuals\/processing-stocks-data-in-python-for-the-analysis\/\">Processing Stocks Data in Python for the Analysis<\/a><br>Part 3 Filtering and visualizing the Stocks Data Analysis results in Python<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<div class=\"entry-content-asset\"><iframe loading=\"lazy\" title=\"Python Matplotlib: Plot Performance Grid of Multiple Stocks with Dynamic Subplots | Part 7 \ud83d\uddbc\ufe0f\" width=\"980\" height=\"551\" src=\"https:\/\/www.youtube.com\/embed\/lqeGdfprKn0?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div>\n<\/div><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p class=\"has-text-align-center\"><a class=\"maxbutton-1 maxbutton maxbutton-subscribe-to-api external-css btn\" href=\"https:\/\/eodhd.com\/register\"><span class='mb-text'>Register &amp; Get Data<\/span><\/a><\/p>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">import datetime as dt\nfrom eod import EodHistoricalData\nimport json\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport pandas as pd\nimport requests\n\nDEFAULT_DATE = dt.date.today() - dt.timedelta(396)\nTODAY =dt.date.today()\n\ndef plot_performance(folder):\n    \"\"\"\n    returns figure containing relative performance of all securities in folder\n    \"\"\"\n    files = [file for file in os.listdir(folder) if not file.startswith('0')]\n    fig, ax = plt.subplots(math.ceil(len(files)\/ 4), 4, figsize=(16,16))\n    count = 0\n    for row in range(math.ceil(len(files)\/ 4)):\n        for column in range(4):\n            try:\n                data = pd.read_csv(f\"{folder}\/{files[count]}\")['close']\n                data = (data\/data[0] -1) * 100\n                ax<div class=\"row\"><\/div>.plot(data, label= files[count][:-4])\n                ax<div class=\"row\"><\/div>.legend()\n                ax<div class=\"row\"><\/div>.yaxis.set_major_formatter(mtick.PercentFormatter())\n                ax<div class=\"row\"><\/div>.axhline(0, c='r', ls='--')\n            except:\n                pass\n            count +=1\n    plt.show()  \n\ndef main():\n    key = open('api_token.txt').read()\n    plot_performance('energy')\n\nif __name__ == '__main__':\n    main()<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<div class=\"entry-content-asset\"><iframe loading=\"lazy\" title=\"Python Financial Events: Find Stocks Announcing Earnings or Dividends (EOD API) | Part 8 \ud83d\udcc5\" width=\"980\" height=\"551\" src=\"https:\/\/www.youtube.com\/embed\/qkLBdazECss?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div>\n<\/div><\/figure>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">import datetime as dt\nfrom eod import EodHistoricalData\nimport json\nimport math\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as mtick\nimport numpy as np\nimport os\nimport pandas as pd\nimport requests\n\nDEFAULT_DATE = dt.date.today() - dt.timedelta(396)\nTODAY =dt.date.today()\n\ndef get_earnings(key):\n    \"\"\"\n    returns list of tickers for companies reporting in the next week\n    \"\"\"\n    client =EodHistoricalData(key)\n    eps = pd.DataFrame(client.get_calendar_earnings())\n    symbols =[]\n\n    for row in range(len(eps)):\n        if eps.earnings.iloc<div class=\"row\"><\/div>['code'].endswith('US'):\n            symbols.append(eps.earnings<div class=\"row\"><\/div>['code'][:-3])\n    print(f\"There are {len(symbols)} companies reporting this week\")\n    return symbols        \n\n\ndef get_dividends(key, exchange ='US', date= dt.date.today()):\n    \"\"\"\n    returns securities with specific ex-date\n    \"\"\"\n    client = EodHistoricalData(key)\n    data = pd.DataFrame(client.get_bulk_markets(exchange= exchange,\n                        date= date, type_='dividends'))\n    return data                    \n\ndef main():\n    key = open('api_token.txt').read()\n    print(get_earnings(key))\n    print(get_dividends(key ))\n\nif __name__ == '__main__':\n    main()<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<div class=\"entry-content-asset\"><iframe loading=\"lazy\" title=\"Python Stock Screener: Calculate Close to 52-Week High Ratio (EOD API &amp; Pandas) | Part 9 \ud83d\udd0e\" width=\"980\" height=\"551\" src=\"https:\/\/www.youtube.com\/embed\/oVKevwdOl4A?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div>\n<\/div><\/figure>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">import datetime as dt\nfrom eod import EodHistoricalData\nimport json\nimport math\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as mtick\nimport numpy as np\nimport os\nimport pandas as pd\nimport requests\n\nDEFAULT_DATE = dt.date.today() - dt.timedelta(396)\nTODAY =dt.date.today()\n\ndef screen_example(symbols, key):\n    \"\"\"\n    Returns DataFrame with current price, 52-week high and ratio \n    of high to current price. Symbols is list-like\n    \"\"\"\n    high = {}\n    call = f\"https:\/\/eodhistoricaldata.com\/api\/eod-bulk-last-day\/US?api_token={key}&amp;fmt=json\"\n    data = pd.DataFrame(requests.get(call).json())\n    data.reset_index(drop=True)\n\n    client = EodHistoricalData(key)\n    for ticker in symbols:\n        try:\n            high[ticker] = client.get_fundamental_equity(f\"{ticker}.US\")['Technicals']['52WeekHigh']\n            print(f\"fetching {ticker}\")\n        except:\n            print(f\"{ticker} not available skipping\")\n        finally:\n            print(f\"All available data downloaded\")\n    mask = data.code.isin(symbols)\n    prices = data[['code', 'close']][mask]\n    high = pd.Series(high, name='high')\n    prices = prices.merge(high, right_on=high.index, left_on ='code')\n    prices['ratio'] = prices['close'] \/ prices['high']\n    return prices            \n\ndef main():\n    key = open('api_token.txt').read()\n    sp = get_sp()[:10]\n    print(screen_example(sp,key))\n\nif __name__ == '__main__':\n    main()<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<div class=\"entry-content-asset\"><iframe loading=\"lazy\" title=\"Python OOP for Finance: Building a Stock Analysis Class to Manage Individual Securities | Part 10 \ud83d\udee0\ufe0f\" width=\"980\" height=\"551\" src=\"https:\/\/www.youtube.com\/embed\/NO4u7HW8uBo?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div>\n<\/div><\/figure>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">from eod import EodHistoricalData\nimport datetime as dt\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as mtick\nimport numpy as np\nimport os\nimport pandas as pd\nimport seaborn as sb\nsb.set_theme()\n\nDEFAULT_DATE = dt.date.isoformat(dt.date.today() - dt.timedelta(396))\n\n\nclass Stock:\n    def __init__(self, symbol, key, date =DEFAULT_DATE, folder=None):\n        self.symbol = symbol\n        self.key = key\n        self.date = date\n        self.folder = folder\n        self.data = self.get_data()\n\n\n    def get_data(self):\n        available_data = [filename[:-4] \n        for filename in os.listdir(self.folder) if not filename.startswith('0')]\n        if self.symbol in available_data:\n            data = pd.read_csv(f\"{self.folder}\/{self.symbol}.csv\", \n                                index_col='date').round(2)\n        else:\n            client = EodHistoricalData(self.key)\n            data = pd.DataFrame(client.get_prices_eod(self.symbol,\n                                from_=self.date)).round(2)\n            data.index = pd.DatetimeIndex(data.date).date\n            data.drop(columns=['date'], inplace = True)\n\n        return data   \n\ndef main():\n    KEY = open('api_token.txt').read()\n    test = Stock(symbol='AAPL', key=KEY)\n    print(test.data)\n\nif __name__ == '__main__':\n    main()<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<div class=\"entry-content-asset\"><iframe loading=\"lazy\" title=\"Python Class: Calculate Log Returns, Rolling Volatility, &amp; Plot Distribution | Part 11 \ud83d\udcca\" width=\"980\" height=\"551\" src=\"https:\/\/www.youtube.com\/embed\/XFuOftGQZY4?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div>\n<\/div><\/figure>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">from eod import EodHistoricalData\nimport datetime as dt\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as mtick\nimport numpy as np\nimport os\nimport pandas as pd\nimport seaborn as sb\nsb.set_theme()\n\nDEFAULT_DATE = dt.date.isoformat(dt.date.today() - dt.timedelta(396))\n\nclass Stock:\n    def __init__(self, symbol, key, date =DEFAULT_DATE, folder=None):\n        self.symbol = symbol\n        self.key = key\n        self.date = date\n        self.folder = folder\n        self.data = self.get_data()\n\n\n    def get_data(self):\n        available_data = [filename[:-4] \n        for filename in os.listdir(self.folder) if not filename.startswith('0')]\n        if self.symbol in available_data:\n            data = pd.read_csv(f\"{self.folder}\/{self.symbol}.csv\", \n                                index_col='date').round(2)\n        else:\n            client = EodHistoricalData(self.key)\n            data = pd.DataFrame(client.get_prices_eod(self.symbol,\n                                from_=self.date)).round(2)\n            data.index = pd.DatetimeIndex(data.date).date\n            data.drop(columns=['date'], inplace = True)\n            self.calc_vol(data)\n        return data\n\ndef calc_vol(self, df):\n        df['returns'] = np.log(df.close).diff().round(4)\n        df['volatility'] = df.returns.rolling(21).std().round(4)\n        df['change'] = df['close'].diff()\n        df['hi_low_spread'] = ((df['high'] -df['low']) \/ df['open']).round(2)\n        df['exp_change'] = (df.volatility * df.close.shift(1)).round(2)\n        df['magnitude'] = (df.change \/ df.exp_change).round(2)\n        df['abs_magnitude'] = np.abs(df.magnitude)\n        df.dropna(inplace= True)\n\n    def plot_return_dist(self):\n        start = self.data.index[0]\n        end  = self.data.index[-1]\n        plt.hist(self.data['returns'], bins=20, edgecolor='w')\n        plt.suptitle(f\"Distribution of returns for {self.symbol}\", fontsize=14)\n        plt.title(f\"From {start} to {end}\", fontsize=12)\n        plt.show()  \n\ndef main():\n    KEY = open('api_token.txt').read()\n    test = Stock(symbol='AAPL', key=KEY)\n    print(test.data)\n    test.plot_return_dist()\n\nif __name__ == '__main__':\n    main()<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<div class=\"entry-content-asset\"><iframe loading=\"lazy\" title=\"Python Matplotlib: Visualize Returns, Volatility Scatter, &amp; Relative Performance | Part 12 \ud83d\udcc8\" width=\"980\" height=\"551\" src=\"https:\/\/www.youtube.com\/embed\/UyCr6Cd4erw?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div>\n<\/div><\/figure>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">from eod import EodHistoricalData\nimport datetime as dt\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as mtick\nimport numpy as np\nimport os\nimport pandas as pd\nimport seaborn as sb\nsb.set_theme()\n\nDEFAULT_DATE = dt.date.isoformat(dt.date.today() - dt.timedelta(396))\n\nclass Stock:\n    def __init__(self, symbol, key, date =DEFAULT_DATE, folder=None):\n        self.symbol = symbol\n        self.key = key\n        self.date = date\n        self.folder = folder\n        self.data = self.get_data()\n\n\n    def get_data(self):\n        available_data = [filename[:-4] \n        for filename in os.listdir(self.folder) if not filename.startswith('0')]\n        if self.symbol in available_data:\n            data = pd.read_csv(f\"{self.folder}\/{self.symbol}.csv\", \n                                index_col='date').round(2)\n        else:\n            client = EodHistoricalData(self.key)\n            data = pd.DataFrame(client.get_prices_eod(self.symbol,\n                                from_=self.date)).round(2)\n            data.index = pd.DatetimeIndex(data.date).date\n            data.drop(columns=['date'], inplace = True)\n            self.calc_vol(data)\n        return data\n\n    def calc_vol(self, df):\n        df['returns'] = np.log(df.close).diff().round(4)\n        df['volatility'] = df.returns.rolling(21).std().round(4)\n        df['change'] = df['close'].diff()\n        df['hi_low_spread'] = ((df['high'] -df['low']) \/ df['open']).round(2)\n        df['exp_change'] = (df.volatility * df.close.shift(1)).round(2)\n        df['magnitude'] = (df.change \/ df.exp_change).round(2)\n        df['abs_magnitude'] = np.abs(df.magnitude)\n        df.dropna(inplace= True)\n\n    def plot_return_dist(self):\n        start = self.data.index[0]\n        end  = self.data.index[-1]\n        plt.hist(self.data['returns'], bins=20, edgecolor='w')\n        plt.suptitle(f\"Distribution of returns for {self.symbol}\", fontsize=14)\n        plt.title(f\"From {start} to {end}\", fontsize=12)\n        plt.show()\n\n    def plot_volatility(self):\n        start = self.data.index[0]\n        end  = self.data.index[-1]\n        plt.scatter(self.data['returns'], self.data['abs_magnitude'])\n        plt.axhline(0, c='r', ls='--')\n        plt.axvline(0, c='r', ls='--')\n        plt.suptitle(f\"Volatility of returns for {self.symbol}\", fontsize=14)\n        plt.title(f\"From {start} to {end}\", fontsize=12)\n        plt.show()\n\n    def plot_performance(self):\n        start = self.data.index[0]\n        end  = self.data.index[-1]\n        plt.plot((self.data.close \/ self.data.close[0] - 1) * 100)\n        plt.axhline(0, c='r', ls='--')\n        plt.suptitle(f\"Volatility of returns for {self.symbol}\", fontsize=14)\n        plt.title(f\"From {start} to {end}\", fontsize=12)\n        plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())\n        plt.show()    \n\ndef main():\n    KEY = open('api_token.txt').read()\n    test = Stock(symbol='AAPL', key=KEY)\n    test.plot_volatility()\n    test.plot_performance()\n\nif __name__ == '__main__':\n    main()<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<div class=\"entry-content-asset\"><iframe loading=\"lazy\" title=\"Python Pandas: Filter Time Series Data by Option Expiration &amp; Low Volatility Duration | Part 13 \ud83d\udcc5\" width=\"980\" height=\"551\" src=\"https:\/\/www.youtube.com\/embed\/CMMll9d9SUU?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div>\n<\/div><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p class=\"has-text-align-center\"><a class=\"maxbutton-1 maxbutton maxbutton-subscribe-to-api external-css btn\" href=\"https:\/\/eodhd.com\/register\"><span class='mb-text'>Register &amp; Get Data<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Preface: In this video series Matt shows how to use the EODHD Financial APIs for the Stock Analysis with Python. Don&#8217;t forget to explore other 2 Parts to learn more:Part 1 Obtaining Stocks Data for the Analysis in PythonPart 2 Processing Stocks Data in Python for the AnalysisPart 3 Filtering and visualizing the Stocks Data [&hellip;]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[42],"tags":[51],"coding-language":[30],"ready-to-go-solution":[],"qualification":[31,32],"financial-apis-category":[36],"financial-apis-manuals":[39,47,37,40],"class_list":["post-479","post","type-post","status-publish","format-standard","hentry","category-stocks-data-analysis-examples","tag-youtube","coding-language-python","qualification-experienced","qualification-guru","financial-apis-category-stock-market-prices","financial-apis-manuals-end-of-day","financial-apis-manuals-exchanges-data","financial-apis-manuals-stocks-fundamentals","financial-apis-manuals-technical-indicators"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.9 (Yoast SEO v26.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How to Visualize Stocks Data using Python | EODHD APIs Academy<\/title>\n<meta name=\"description\" content=\"Master the art of visualizing stocks data in Python. 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