SQL_and_BigQuery_day2.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"**[SQL Home Page](https://www.kaggle.com/learn/intro-to-sql)**\n\n---\n"},{"metadata":{},"cell_type":"markdown","source":"# Introduction\n\nTry writing some **SELECT** statements of your own to explore a large dataset of air pollution measurements.\n\nRun the cell below to set up the feedback system."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Set up feedback system\nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.sql.ex2 import *\nprint(\"Setup Complete\")","execution_count":1,"outputs":[{"output_type":"stream","text":"Using Kaggle's public dataset BigQuery integration.\nSetup Complete\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"The code cell below fetches the `global_air_quality` table from the `openaq` dataset.  We also preview the first five rows of the table."},{"metadata":{"trusted":true},"cell_type":"code","source":"from google.cloud import bigquery\n\n# Create a \"Client\" object\nclient = bigquery.Client()\n\n# Construct a reference to the \"openaq\" dataset\ndataset_ref = client.dataset(\"openaq\", project=\"bigquery-public-data\")\n\n# API request - fetch the dataset\ndataset = client.get_dataset(dataset_ref)\n\ntables = list(client.list_tables(dataset))\ncount=0\nfor table in tables:\n    count+=1\n    print(table.table_id)\nprint(\"Number of tables: \"+str(count))\n","execution_count":3,"outputs":[{"output_type":"stream","text":"Using Kaggle's public dataset BigQuery integration.\nglobal_air_quality\nNumber of tables: 1\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n# Construct a reference to the \"global_air_quality\" table\ntable_ref = dataset_ref.table(\"global_air_quality\")\n\n# API request - fetch the table\ntable = client.get_table(table_ref)\n\n# Preview the first five lines of the \"global_air_quality\" table\nclient.list_rows(table, max_results=5).to_dataframe()","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"                        location          ...           averaged_over_in_hours\n0  BTM Layout, Bengaluru - KSPCB          ...                             0.25\n1  BTM Layout, Bengaluru - KSPCB          ...                             0.25\n2  BTM Layout, Bengaluru - KSPCB          ...                             0.25\n3  BTM Layout, Bengaluru - KSPCB          ...                             0.25\n4  BTM Layout, Bengaluru - KSPCB          ...                             0.25\n\n[5 rows x 11 columns]","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>location</th>\n      <th>city</th>\n      <th>country</th>\n      <th>pollutant</th>\n      <th>value</th>\n      <th>timestamp</th>\n      <th>unit</th>\n      <th>source_name</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>averaged_over_in_hours</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>BTM Layout, Bengaluru - KSPCB</td>\n      <td>Bengaluru</td>\n      <td>IN</td>\n      <td>co</td>\n      <td>910.00</td>\n      <td>2018-02-22 03:00:00+00:00</td>\n      <td>µg/m³</td>\n      <td>CPCB</td>\n      <td>12.912811</td>\n      <td>77.60922</td>\n      <td>0.25</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>BTM Layout, Bengaluru - KSPCB</td>\n      <td>Bengaluru</td>\n      <td>IN</td>\n      <td>no2</td>\n      <td>131.87</td>\n      <td>2018-02-22 03:00:00+00:00</td>\n      <td>µg/m³</td>\n      <td>CPCB</td>\n      <td>12.912811</td>\n      <td>77.60922</td>\n      <td>0.25</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>BTM Layout, Bengaluru - KSPCB</td>\n      <td>Bengaluru</td>\n      <td>IN</td>\n      <td>o3</td>\n      <td>15.57</td>\n      <td>2018-02-22 03:00:00+00:00</td>\n      <td>µg/m³</td>\n      <td>CPCB</td>\n      <td>12.912811</td>\n      <td>77.60922</td>\n      <td>0.25</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>BTM Layout, Bengaluru - KSPCB</td>\n      <td>Bengaluru</td>\n      <td>IN</td>\n      <td>pm25</td>\n      <td>45.62</td>\n      <td>2018-02-22 03:00:00+00:00</td>\n      <td>µg/m³</td>\n      <td>CPCB</td>\n      <td>12.912811</td>\n      <td>77.60922</td>\n      <td>0.25</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>BTM Layout, Bengaluru - KSPCB</td>\n      <td>Bengaluru</td>\n      <td>IN</td>\n      <td>so2</td>\n      <td>4.49</td>\n      <td>2018-02-22 03:00:00+00:00</td>\n      <td>µg/m³</td>\n      <td>CPCB</td>\n      <td>12.912811</td>\n      <td>77.60922</td>\n      <td>0.25</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Exercises\n\n### 1) Units of measurement\n\nWhich countries have reported pollution levels in units of \"ppm\"?  In the code cell below, set `first_query` to an SQL query that pulls the appropriate entries from the `country` column.\n\nIn case it's useful to see an example query, here's some code from the tutorial:\n\n```\nquery = \"\"\"\n        SELECT city\n        FROM `bigquery-public-data.openaq.global_air_quality`\n        WHERE country = 'US'\n        \"\"\"\n```"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Query to select countries with units of \"ppm\"\nfirst_query = \"\"\"\n        SELECT DISTINCT country\n        FROM `bigquery-public-data.openaq.global_air_quality`\n        WHERE unit = 'ppm'\n        \"\"\"\n\n\n# Set up the query (cancel the query if it would use too much of \n# your quota, with the limit set to 10 GB)\nsafe_config = bigquery.QueryJobConfig(maximum_bytes_billed=10**10)\nfirst_query_job = client.query(first_query, job_config=safe_config)\n\n# API request - run the query, and return a pandas DataFrame\nfirst_results = first_query_job.to_dataframe()\n\n# View top few rows of results\nprint(first_results.head())\n\n# Check your answer\nq_1.check()","execution_count":6,"outputs":[{"output_type":"stream","text":"  country\n0      US\n1      MX\n2      CA\n3      IL\n4      TW\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.5, \"interactionType\": 1, \"questionType\": 2, \"learnTutorialId\": 78, \"questionId\": \"1_WhichCountries\", \"learnToolsVersion\": \"0.3.1\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"For the solution, uncomment the line below."},{"metadata":{"trusted":false},"cell_type":"code","source":"#q_1.solution()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 2) High air quality\n\nWhich pollution levels were reported to be exactly 0?  \n- Set `zero_pollution_query` to select **all columns** of the rows where the `value` column is 0.\n- Set `zero_pollution_results` to a pandas DataFrame containing the query results."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Query to select all columns where pollution levels are exactly 0\nzero_pollution_query = \"\"\"\n        SELECT *\n        FROM `bigquery-public-data.openaq.global_air_quality`\n        WHERE value = 0\n        \"\"\"\n\n# Set up the query\nsafe_config = bigquery.QueryJobConfig(maximum_bytes_billed=10**10)\nquery_job = client.query(zero_pollution_query, job_config=safe_config)\n\n# API request - run the query and return a pandas DataFrame\nzero_pollution_results = query_job.to_dataframe()\n\nprint(zero_pollution_results.head())\n\n# Check your answer\nq_2.check()","execution_count":8,"outputs":[{"output_type":"stream","text":"                                        location          ...           averaged_over_in_hours\n0                     Victoria Memorial - WBSPCB          ...                             0.25\n1  Rabindra Bharati University, Kolkata - WBSPCB          ...                             0.25\n2                               Końskie, MOBILNA          ...                              NaN\n3                               Końskie, MOBILNA          ...                              NaN\n4                               Włocławek OKRZEI          ...                              NaN\n\n[5 rows x 11 columns]\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.5, \"interactionType\": 1, \"questionType\": 2, \"learnTutorialId\": 78, \"questionId\": \"2_ZeroPollution\", \"learnToolsVersion\": \"0.3.1\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"For the solution, uncomment the line below."},{"metadata":{"trusted":false},"cell_type":"code","source":"#q_2.solution()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"That query wasn't too complicated, and it got the data you want. But these **SELECT** queries don't organizing data in a way that answers the most interesting questions. For that, we'll need the **GROUP BY** command. \n\nIf you know how to use [`groupby()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html) in pandas, this is similar. But BigQuery works quickly with far larger datasets.\n\nFortunately, that's next."},{"metadata":{},"cell_type":"markdown","source":"# Keep going\n**[GROUP BY](https://www.kaggle.com/dansbecker/group-by-having-count)** clauses and their extensions give you the power to pull interesting statistics out of data, rather than receiving it in just its raw format."},{"metadata":{"trusted":true},"cell_type":"code","source":"zero_pollution_results.groupby(['city']).mean()","execution_count":13,"outputs":[{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"                                                value           ...            averaged_over_in_hours\ncity                                                            ...                                  \n007                                               0.0           ...                              1.00\nA Coruña                                          0.0           ...                              1.00\nADAIR                                             0.0           ...                              1.00\nADAMS                                             0.0           ...                              1.00\nAIR NORMAND                                       0.0           ...                              1.00\nAIR PACA                                          0.0           ...                              1.00\nAIR RHONE ALPES                                   0.0           ...                              1.00\nALBERTA                                           0.0           ...                              1.00\nAROOSTOOK                                         0.0           ...                              1.00\nATMO NORD-PAS-DE-CALAIS                           0.0           ...                              1.00\nATMO NOUVELLE-AQUITAINE                           0.0           ...                              1.00\nAdana                                             0.0           ...                              1.00\nAkron                                             0.0           ...                              1.00\nAlbacete                                          0.0           ...                              1.00\nAlicante/Alacant                                  0.0           ...                              1.00\nAlmería                                           0.0           ...                              1.00\nAlytus                                            0.0           ...                              1.00\nAmasya                                            0.0           ...                              1.00\nAmt der K�rntner Landesregierung                  0.0           ...                              1.00\nAmt der Nieder�sterreichischen Landesregierung    0.0           ...                              1.00\nAmt der Oberösterreichischen Landesregierung      0.0           ...                              1.00\nAmt der Ober�sterreichischen Landesregierung      0.0           ...                              1.00\nAmt der Salzburger Landesregierung                0.0           ...                              1.00\nAmt der Steierm�rkischen Landesregierung          0.0           ...                              1.00\nAmt der Tiroler Landesregierung                   0.0           ...                              1.00\nAnkara                                            0.0           ...                              1.00\nAntwerpen                                         0.0           ...                              1.00\nAraba/Álava                                       0.0           ...                              1.00\nArcos De La Frontera                              0.0           ...                              1.00\nAsheville                                         0.0           ...                              1.00\n...                                               ...           ...                               ...\nYORK                                              0.0           ...                              1.00\nYakima                                            0.0           ...                              1.00\nYoungstown-Warren-Boardman                        0.0           ...                              1.00\nZamora                                            0.0           ...                              1.00\nZaragoza                                          0.0           ...                              1.00\nZenica                                            0.0           ...                              1.00\nZonguldak                                         0.0           ...                              1.00\nZürich                                            0.0           ...                              1.00\nÁvila                                             0.0           ...                              1.00\nÅlesund                                           0.0           ...                              1.00\nÚstecký                                           0.0           ...                              1.00\nİstanbul                                          0.0           ...                              1.00\nİzmir                                             0.0           ...                              1.00\nŠiauliai                                          0.0           ...                              1.00\nגוש דן                                            0.0           ...                              0.25\nירושלים                                           0.0           ...                              0.25\nמישור החוף הדרומי                                 0.0           ...                              0.25\nצפון הנגב                                         0.0           ...                              0.25\nשרון                                              0.0           ...                              0.25\nתחנות ניידות                                      0.0           ...                              0.25\n乌兰察布市                                             0.0           ...                              1.00\n九江市                                               0.0           ...                              1.00\n佳木斯市                                              0.0           ...                              1.00\n天水市                                               0.0           ...                              1.00\n拉萨市                                               0.0           ...                              1.00\n日喀则市                                              0.0           ...                              1.00\n昌都市                                               0.0           ...                              1.00\n本溪市                                               0.0           ...                              1.00\n牡丹江市                                              0.0           ...                              1.00\n黔南州                                               0.0           ...                              1.00\n\n[483 rows x 4 columns]","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>value</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>averaged_over_in_hours</th>\n    </tr>\n    <tr>\n      <th>city</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>007</th>\n      <td>0.0</td>\n      <td>47.768610</td>\n      <td>-120.664450</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>A Coruña</th>\n      <td>0.0</td>\n      <td>43.104939</td>\n      <td>-8.491574</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>ADAIR</th>\n      <td>0.0</td>\n      <td>35.750600</td>\n      <td>-94.669700</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>ADAMS</th>\n      <td>0.0</td>\n      <td>38.794710</td>\n      <td>-83.533990</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>AIR NORMAND</th>\n      <td>0.0</td>\n      <td>49.441444</td>\n      <td>1.082658</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>AIR PACA</th>\n      <td>0.0</td>\n      <td>43.403139</td>\n      <td>5.099353</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>AIR RHONE ALPES</th>\n      <td>0.0</td>\n      <td>45.473035</td>\n      <td>4.635415</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>ALBERTA</th>\n      <td>0.0</td>\n      <td>53.495654</td>\n      <td>-114.249526</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>AROOSTOOK</th>\n      <td>0.0</td>\n      <td>46.696400</td>\n      <td>-68.033100</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>ATMO NORD-PAS-DE-CALAIS</th>\n      <td>0.0</td>\n      <td>50.886381</td>\n      <td>2.789552</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>ATMO NOUVELLE-AQUITAINE</th>\n      <td>0.0</td>\n      <td>43.376137</td>\n      <td>-0.536944</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Adana</th>\n      <td>0.0</td>\n      <td>36.854534</td>\n      <td>35.349144</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Akron</th>\n      <td>0.0</td>\n      <td>41.083051</td>\n      <td>-81.496259</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Albacete</th>\n      <td>0.0</td>\n      <td>38.979280</td>\n      <td>-1.852130</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Alicante/Alacant</th>\n      <td>0.0</td>\n      <td>38.355210</td>\n      <td>-0.604625</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Almería</th>\n      <td>0.0</td>\n      <td>36.937352</td>\n      <td>-2.201772</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Alytus</th>\n      <td>0.0</td>\n      <td>54.092500</td>\n      <td>24.289728</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Amasya</th>\n      <td>0.0</td>\n      <td>40.874332</td>\n      <td>35.457474</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Amt der K�rntner Landesregierung</th>\n      <td>0.0</td>\n      <td>46.692222</td>\n      <td>14.299352</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Amt der Nieder�sterreichischen Landesregierung</th>\n      <td>0.0</td>\n      <td>48.087779</td>\n      <td>16.275555</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Amt der Oberösterreichischen Landesregierung</th>\n      <td>0.0</td>\n      <td>48.302776</td>\n      <td>14.282778</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Amt der Ober�sterreichischen Landesregierung</th>\n      <td>0.0</td>\n      <td>48.350280</td>\n      <td>14.377143</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Amt der Salzburger Landesregierung</th>\n      <td>0.0</td>\n      <td>47.584305</td>\n      <td>13.118194</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Amt der Steierm�rkischen Landesregierung</th>\n      <td>0.0</td>\n      <td>47.386211</td>\n      <td>14.670167</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Amt der Tiroler Landesregierung</th>\n      <td>0.0</td>\n      <td>47.305622</td>\n      <td>11.375998</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Ankara</th>\n      <td>0.0</td>\n      <td>39.936947</td>\n      <td>32.878166</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Antwerpen</th>\n      <td>0.0</td>\n      <td>51.235157</td>\n      <td>4.490403</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Araba/Álava</th>\n      <td>0.0</td>\n      <td>42.873185</td>\n      <td>-2.816468</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Arcos De La Frontera</th>\n      <td>0.0</td>\n      <td>36.739750</td>\n      <td>-5.743722</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Asheville</th>\n      <td>0.0</td>\n      <td>35.481860</td>\n      <td>-82.509865</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>YORK</th>\n      <td>0.0</td>\n      <td>34.977000</td>\n      <td>-81.207000</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Yakima</th>\n      <td>0.0</td>\n      <td>46.594950</td>\n      <td>-120.512280</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Youngstown-Warren-Boardman</th>\n      <td>0.0</td>\n      <td>41.096157</td>\n      <td>-80.658905</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Zamora</th>\n      <td>0.0</td>\n      <td>41.509724</td>\n      <td>-5.746389</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Zaragoza</th>\n      <td>0.0</td>\n      <td>41.648404</td>\n      <td>-0.860926</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Zenica</th>\n      <td>0.0</td>\n      <td>44.202000</td>\n      <td>17.800000</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Zonguldak</th>\n      <td>0.0</td>\n      <td>41.443429</td>\n      <td>31.769470</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Zürich</th>\n      <td>0.0</td>\n      <td>47.377617</td>\n      <td>8.530411</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Ávila</th>\n      <td>0.0</td>\n      <td>40.664720</td>\n      <td>-4.700830</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Ålesund</th>\n      <td>0.0</td>\n      <td>62.471000</td>\n      <td>6.156650</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Ústecký</th>\n      <td>0.0</td>\n      <td>50.585766</td>\n      <td>13.673418</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>İstanbul</th>\n      <td>0.0</td>\n      <td>41.059568</td>\n      <td>28.934523</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>İzmir</th>\n      <td>0.0</td>\n      <td>38.432220</td>\n      <td>27.144444</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>Šiauliai</th>\n      <td>0.0</td>\n      <td>56.080938</td>\n      <td>23.144082</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>גוש דן</th>\n      <td>0.0</td>\n      <td>32.081077</td>\n      <td>34.823520</td>\n      <td>0.25</td>\n    </tr>\n    <tr>\n      <th>ירושלים</th>\n      <td>0.0</td>\n      <td>31.780100</td>\n      <td>35.223930</td>\n      <td>0.25</td>\n    </tr>\n    <tr>\n      <th>מישור החוף הדרומי</th>\n      <td>0.0</td>\n      <td>31.665130</td>\n      <td>34.711235</td>\n      <td>0.25</td>\n    </tr>\n    <tr>\n      <th>צפון הנגב</th>\n      <td>0.0</td>\n      <td>31.066310</td>\n      <td>34.836760</td>\n      <td>0.25</td>\n    </tr>\n    <tr>\n      <th>שרון</th>\n      <td>0.0</td>\n      <td>32.498540</td>\n      <td>34.934200</td>\n      <td>0.25</td>\n    </tr>\n    <tr>\n      <th>תחנות ניידות</th>\n      <td>0.0</td>\n      <td>31.845780</td>\n      <td>34.685017</td>\n      <td>0.25</td>\n    </tr>\n    <tr>\n      <th>乌兰察布市</th>\n      <td>0.0</td>\n      <td>41.038500</td>\n      <td>113.107600</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>九江市</th>\n      <td>0.0</td>\n      <td>29.699500</td>\n      <td>116.025700</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>佳木斯市</th>\n      <td>0.0</td>\n      <td>46.826700</td>\n      <td>130.396100</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>天水市</th>\n      <td>0.0</td>\n      <td>34.346900</td>\n      <td>106.005000</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>拉萨市</th>\n      <td>0.0</td>\n      <td>29.651400</td>\n      <td>91.131900</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>日喀则市</th>\n      <td>0.0</td>\n      <td>29.236500</td>\n      <td>88.893100</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>昌都市</th>\n      <td>0.0</td>\n      <td>31.127800</td>\n      <td>97.180400</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>本溪市</th>\n      <td>0.0</td>\n      <td>41.347200</td>\n      <td>123.814200</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>牡丹江市</th>\n      <td>0.0</td>\n      <td>44.595200</td>\n      <td>129.590200</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>黔南州</th>\n      <td>0.0</td>\n      <td>26.240300</td>\n      <td>107.522800</td>\n      <td>1.00</td>\n    </tr>\n  </tbody>\n</table>\n<p>483 rows × 4 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"---\n**[SQL Home Page](https://www.kaggle.com/learn/intro-to-sql)**\n\n\n\n\n\n*Have questions or comments? Visit the [Learn Discussion forum](https://www.kaggle.com/learn-forum) to chat with other Learners.*"},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"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":"3.6.5"}},"nbformat":4,"nbformat_minor":1}