analysis_of_sec.ipynb

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"from google.cloud import bigquery\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport wordcloud\n\n\nPROJECT_ID = 'your_project_id'  \nclient = bigquery.Client(project=PROJECT_ID, location=\"US\")\n\ndataset_ref = client.dataset(\"sec_quarterly_financials\", project=\"bigquery-public-data\")\n\n# Make an API request to fetch the dataset\ndataset = client.get_dataset(dataset_ref)","execution_count":24,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"tables = list(client.list_tables(dataset))\n\n# Print names of all tables in the dataset\nquick_summary = None\nfor table in tables:\n    print(table.table_id)\n    if table.table_id == \"quick_summary\":\n        quick_summary = table","execution_count":25,"outputs":[{"output_type":"stream","text":"calculation\ndimension\nmeasure_tag\nnumbers\npresentation\nquick_summary\nrendering\nsic_codes\nsubmission\ntxt\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"client.list_rows(quick_summary, max_results=5).to_dataframe()","execution_count":26,"outputs":[{"output_type":"execute_result","execution_count":26,"data":{"text/plain":"      submission_number                         company_name  \\\n0  0001437749-17-000102               NATIONAL HOLDINGS CORP   \n1  0001062993-17-000442  XIANGTIAN (USA) AIR POWER CO., LTD.   \n2  0001564590-17-000589              SCHLUMBERGER LIMITED/NV   \n3  0001558370-17-000064                    HELEN OF TROY LTD   \n4  0001674862-17-000006          ASHLAND GLOBAL HOLDINGS INC   \n\n                                         measure_tag period_end_date  \\\n0                                        Liabilities        20160930   \n1                                    CostOfGoodsSold        20140731   \n2    DeferredTaxLiabilitiesPropertyPlantAndEquipment        20161231   \n3  OtherComprehensiveIncomeLossNetOfTaxPortionAtt...        20151130   \n4               DepreciationDepletionAndAmortization        20161231   \n\n        value units  number_of_quarters       version  central_index_key  \\\n0  34773000.0   USD                   0  us-gaap/2016            1023844   \n1         0.0   USD                   4  us-gaap/2015            1472468   \n2  79000000.0   USD                   0  us-gaap/2016              87347   \n3   1805000.0   USD                   3  us-gaap/2016             916789   \n4  77000000.0   USD                   1  us-gaap/2015            1674862   \n\n         ein   sic fiscal_year_end    form  fiscal_year fiscal_period_focus  \\\n0  364128138  6200            0930   S-1/A         2016                  FY   \n1  980632932  3621            0731  POS AM         2017                  Q1   \n2  520684746  1389            1231    10-K         2016                  FY   \n3  742692550  3634            0229    10-Q         2016                  Q3   \n4  812587835  5160            0930    10-Q         2017                  Q1   \n\n   date_filed             date_accepted  \n0    20170104 2017-01-04 12:20:00+00:00  \n1    20170131 2017-01-30 17:34:00+00:00  \n2    20170125 2017-01-25 11:34:00+00:00  \n3    20170109 2017-01-09 16:00:00+00:00  \n4    20170127 2017-01-27 16:04:00+00:00  ","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    .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>submission_number</th>\n      <th>company_name</th>\n      <th>measure_tag</th>\n      <th>period_end_date</th>\n      <th>value</th>\n      <th>units</th>\n      <th>number_of_quarters</th>\n      <th>version</th>\n      <th>central_index_key</th>\n      <th>ein</th>\n      <th>sic</th>\n      <th>fiscal_year_end</th>\n      <th>form</th>\n      <th>fiscal_year</th>\n      <th>fiscal_period_focus</th>\n      <th>date_filed</th>\n      <th>date_accepted</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0001437749-17-000102</td>\n      <td>NATIONAL HOLDINGS CORP</td>\n      <td>Liabilities</td>\n      <td>20160930</td>\n      <td>34773000.0</td>\n      <td>USD</td>\n      <td>0</td>\n      <td>us-gaap/2016</td>\n      <td>1023844</td>\n      <td>364128138</td>\n      <td>6200</td>\n      <td>0930</td>\n      <td>S-1/A</td>\n      <td>2016</td>\n      <td>FY</td>\n      <td>20170104</td>\n      <td>2017-01-04 12:20:00+00:00</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0001062993-17-000442</td>\n      <td>XIANGTIAN (USA) AIR POWER CO., LTD.</td>\n      <td>CostOfGoodsSold</td>\n      <td>20140731</td>\n      <td>0.0</td>\n      <td>USD</td>\n      <td>4</td>\n      <td>us-gaap/2015</td>\n      <td>1472468</td>\n      <td>980632932</td>\n      <td>3621</td>\n      <td>0731</td>\n      <td>POS AM</td>\n      <td>2017</td>\n      <td>Q1</td>\n      <td>20170131</td>\n      <td>2017-01-30 17:34:00+00:00</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0001564590-17-000589</td>\n      <td>SCHLUMBERGER LIMITED/NV</td>\n      <td>DeferredTaxLiabilitiesPropertyPlantAndEquipment</td>\n      <td>20161231</td>\n      <td>79000000.0</td>\n      <td>USD</td>\n      <td>0</td>\n      <td>us-gaap/2016</td>\n      <td>87347</td>\n      <td>520684746</td>\n      <td>1389</td>\n      <td>1231</td>\n      <td>10-K</td>\n      <td>2016</td>\n      <td>FY</td>\n      <td>20170125</td>\n      <td>2017-01-25 11:34:00+00:00</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0001558370-17-000064</td>\n      <td>HELEN OF TROY LTD</td>\n      <td>OtherComprehensiveIncomeLossNetOfTaxPortionAtt...</td>\n      <td>20151130</td>\n      <td>1805000.0</td>\n      <td>USD</td>\n      <td>3</td>\n      <td>us-gaap/2016</td>\n      <td>916789</td>\n      <td>742692550</td>\n      <td>3634</td>\n      <td>0229</td>\n      <td>10-Q</td>\n      <td>2016</td>\n      <td>Q3</td>\n      <td>20170109</td>\n      <td>2017-01-09 16:00:00+00:00</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0001674862-17-000006</td>\n      <td>ASHLAND GLOBAL HOLDINGS INC</td>\n      <td>DepreciationDepletionAndAmortization</td>\n      <td>20161231</td>\n      <td>77000000.0</td>\n      <td>USD</td>\n      <td>1</td>\n      <td>us-gaap/2015</td>\n      <td>1674862</td>\n      <td>812587835</td>\n      <td>5160</td>\n      <td>0930</td>\n      <td>10-Q</td>\n      <td>2017</td>\n      <td>Q1</td>\n      <td>20170127</td>\n      <td>2017-01-27 16:04:00+00:00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"query1 = \"\"\"\nSELECT \n  company_name,\n  fiscal_year,\n  measure_tag,\n  MAX(value) / 1e9 AS revenue\nFROM\n  `bigquery-public-data.sec_quarterly_financials.quick_summary`\nWHERE\n  company_name IN ('WHOLE FOODS MARKET INC', 'ALPHABET INC.',\n                   'AMAZON COM INC', 'PFIZER INC',\n                   'BANK OF AMERICA CORP /DE/')\n  AND fiscal_year IN (2015, 2016, 2017)\n  AND measure_tag IN ('Revenues', 'SalesRevenueNet',\n                      'SalesRevenueGoodsNet')\n  AND fiscal_period_focus = 'FY'\n  AND number_of_quarters = 4\nGROUP BY\n  company_name, fiscal_year, measure_tag\nORDER BY\n  company_name, fiscal_year\n\"\"\"","execution_count":27,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from google.cloud.bigquery.job import QueryJobConfig\n\n\nquery_job = client.query(query1)\nresult = query_job.to_dataframe()\n\nprint(result)","execution_count":29,"outputs":[{"output_type":"stream","text":"                 company_name  fiscal_year           measure_tag  revenue\n0               ALPHABET INC.         2015              Revenues   74.989\n1               ALPHABET INC.         2016              Revenues   90.272\n2               ALPHABET INC.         2017              Revenues  110.855\n3              AMAZON COM INC         2015       SalesRevenueNet  107.006\n4              AMAZON COM INC         2015  SalesRevenueGoodsNet   79.268\n5              AMAZON COM INC         2016       SalesRevenueNet  135.987\n6              AMAZON COM INC         2016  SalesRevenueGoodsNet   94.665\n7              AMAZON COM INC         2017       SalesRevenueNet  177.866\n8              AMAZON COM INC         2017  SalesRevenueGoodsNet  118.573\n9   BANK OF AMERICA CORP /DE/         2015              Revenues   88.942\n10  BANK OF AMERICA CORP /DE/         2016              Revenues   85.894\n11  BANK OF AMERICA CORP /DE/         2017              Revenues   87.352\n12                 PFIZER INC         2015  SalesRevenueGoodsNet   51.584\n13                 PFIZER INC         2016  SalesRevenueGoodsNet   52.824\n14                 PFIZER INC         2017  SalesRevenueGoodsNet   52.824\n15     WHOLE FOODS MARKET INC         2016       SalesRevenueNet   15.724\n16     WHOLE FOODS MARKET INC         2017       SalesRevenueNet   16.030\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd\n\n\ncomparison = pd.concat(\n    [result[result[\"company_name\"] == \"ALPHABET INC.\"],\n     result[(result[\"company_name\"] == \"AMAZON COM INC\") & (result[\"measure_tag\"] == \"SalesRevenueGoodsNet\")],\n    ], ignore_index=True)\n\nax = sns.barplot(\n    x=\"company_name\",y=\"revenue\", hue=\"fiscal_year\",\n    data=comparison,\n    palette=\"coolwarm\").set_title(\"Annual Revenue\")","execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 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