time-series-exploratory-data-analysis.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":2150,"sourceType":"datasetVersion","datasetId":29}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Imports","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2024-05-31T16:16:54.473342Z","iopub.execute_input":"2024-05-31T16:16:54.474349Z","iopub.status.idle":"2024-05-31T16:16:55.648120Z","shell.execute_reply.started":"2024-05-31T16:16:54.474302Z","shell.execute_reply":"2024-05-31T16:16:55.647088Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"# Constant","metadata":{}},{"cell_type":"code","source":"DATASET_TEMP = \"/kaggle/input/climate-change-earth-surface-temperature-data/GlobalTemperatures.csv\"\nDATASET_TEMP_STATE = \"/kaggle/input/climate-change-earth-surface-temperature-data/GlobalLandTemperaturesByState.csv\"\nDATASET_TEMP_COUNTRY = \"/kaggle/input/climate-change-earth-surface-temperature-data/GlobalLandTemperaturesByCountry.csv\"\nDATASET_TEMP_CITY = \"/kaggle/input/climate-change-earth-surface-temperature-data/GlobalLandTemperaturesByCity.csv\"\nDATASET_TEMP_MJR_CITY = \"/kaggle/input/climate-change-earth-surface-temperature-data/GlobalLandTemperaturesByMajorCity.csv\"","metadata":{"execution":{"iopub.status.busy":"2024-05-31T16:16:55.650389Z","iopub.execute_input":"2024-05-31T16:16:55.650950Z","iopub.status.idle":"2024-05-31T16:16:55.656334Z","shell.execute_reply.started":"2024-05-31T16:16:55.650913Z","shell.execute_reply":"2024-05-31T16:16:55.655227Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"# Code","metadata":{}},{"cell_type":"code","source":"df_temp_global_city = pd.read_csv(DATASET_TEMP_CITY)\ndf_temp_global_city['dt'] = pd.to_datetime(df_temp_global_city['dt'])\ndf_temp_global_city.info()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T16:16:55.658245Z","iopub.execute_input":"2024-05-31T16:16:55.659484Z","iopub.status.idle":"2024-05-31T16:17:14.106257Z","shell.execute_reply.started":"2024-05-31T16:16:55.659442Z","shell.execute_reply":"2024-05-31T16:17:14.105080Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 8599212 entries, 0 to 8599211\nData columns (total 7 columns):\n #   Column                         Dtype         \n---  ------                         -----         \n 0   dt                             datetime64[ns]\n 1   AverageTemperature             float64       \n 2   AverageTemperatureUncertainty  float64       \n 3   City                           object        \n 4   Country                        object        \n 5   Latitude                       object        \n 6   Longitude                      object        \ndtypes: datetime64[ns](1), float64(2), object(4)\nmemory usage: 459.2+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"df_temp_global_city.head(3)","metadata":{"execution":{"iopub.status.busy":"2024-05-31T16:17:14.108823Z","iopub.execute_input":"2024-05-31T16:17:14.109157Z","iopub.status.idle":"2024-05-31T16:17:14.130048Z","shell.execute_reply.started":"2024-05-31T16:17:14.109130Z","shell.execute_reply":"2024-05-31T16:17:14.128666Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"          dt  AverageTemperature  AverageTemperatureUncertainty   City  \\\n0 1743-11-01               6.068                          1.737  Århus   \n1 1743-12-01                 NaN                            NaN  Århus   \n2 1744-01-01                 NaN                            NaN  Århus   \n\n   Country Latitude Longitude  \n0  Denmark   57.05N    10.33E  \n1  Denmark   57.05N    10.33E  \n2  Denmark   57.05N    10.33E  ","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>dt</th>\n      <th>AverageTemperature</th>\n      <th>AverageTemperatureUncertainty</th>\n      <th>City</th>\n      <th>Country</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1743-11-01</td>\n      <td>6.068</td>\n      <td>1.737</td>\n      <td>Århus</td>\n      <td>Denmark</td>\n      <td>57.05N</td>\n      <td>10.33E</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1743-12-01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Århus</td>\n      <td>Denmark</td>\n      <td>57.05N</td>\n      <td>10.33E</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1744-01-01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Århus</td>\n      <td>Denmark</td>\n      <td>57.05N</td>\n      <td>10.33E</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_karachi = df_temp_global_city[(df_temp_global_city['Country'] == 'Pakistan') & (df_temp_global_city['City'] == 'Karachi')]\ndf_karachi.head(4)","metadata":{"execution":{"iopub.status.busy":"2024-05-31T16:17:14.131589Z","iopub.execute_input":"2024-05-31T16:17:14.131898Z","iopub.status.idle":"2024-05-31T16:17:15.423894Z","shell.execute_reply.started":"2024-05-31T16:17:14.131872Z","shell.execute_reply":"2024-05-31T16:17:15.423112Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"                dt  AverageTemperature  AverageTemperatureUncertainty  \\\n3642996 1796-01-01              17.996                          2.362   \n3642997 1796-02-01              20.203                          1.899   \n3642998 1796-03-01              23.303                          2.464   \n3642999 1796-04-01              28.124                          1.912   \n\n            City   Country Latitude Longitude  \n3642996  Karachi  Pakistan   24.92N    67.39E  \n3642997  Karachi  Pakistan   24.92N    67.39E  \n3642998  Karachi  Pakistan   24.92N    67.39E  \n3642999  Karachi  Pakistan   24.92N    67.39E  ","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>dt</th>\n      <th>AverageTemperature</th>\n      <th>AverageTemperatureUncertainty</th>\n      <th>City</th>\n      <th>Country</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3642996</th>\n      <td>1796-01-01</td>\n      <td>17.996</td>\n      <td>2.362</td>\n      <td>Karachi</td>\n      <td>Pakistan</td>\n      <td>24.92N</td>\n      <td>67.39E</td>\n    </tr>\n    <tr>\n      <th>3642997</th>\n      <td>1796-02-01</td>\n      <td>20.203</td>\n      <td>1.899</td>\n      <td>Karachi</td>\n      <td>Pakistan</td>\n      <td>24.92N</td>\n      <td>67.39E</td>\n    </tr>\n    <tr>\n      <th>3642998</th>\n      <td>1796-03-01</td>\n      <td>23.303</td>\n      <td>2.464</td>\n      <td>Karachi</td>\n      <td>Pakistan</td>\n      <td>24.92N</td>\n      <td>67.39E</td>\n    </tr>\n    <tr>\n      <th>3642999</th>\n      <td>1796-04-01</td>\n      <td>28.124</td>\n      <td>1.912</td>\n      <td>Karachi</td>\n      <td>Pakistan</td>\n      <td>24.92N</td>\n      <td>67.39E</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### 1. Check central tendency","metadata":{}},{"cell_type":"code","source":"mean_temp = df_karachi['AverageTemperature'].mean()\nmedian_temp = df_karachi['AverageTemperature'].median()\nmode_temp = df_karachi['AverageTemperature'].mode()[0]  # Mode can have multiple values, take the first\n\nprint(f\"Mean Average Temperature: {mean_temp}\")\nprint(f\"Median Average Temperature: {median_temp}\")\nprint(f\"Mode Average Temperature: {mode_temp}\")","metadata":{"execution":{"iopub.status.busy":"2024-05-31T16:17:15.425240Z","iopub.execute_input":"2024-05-31T16:17:15.425587Z","iopub.status.idle":"2024-05-31T16:17:15.436909Z","shell.execute_reply.started":"2024-05-31T16:17:15.425561Z","shell.execute_reply":"2024-05-31T16:17:15.435574Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Mean Average Temperature: 26.01725903614458\nMedian Average Temperature: 27.588\nMode Average Temperature: 19.126\n","output_type":"stream"}]},{"cell_type":"code","source":"df_karachi.describe()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T16:17:15.438157Z","iopub.execute_input":"2024-05-31T16:17:15.438512Z","iopub.status.idle":"2024-05-31T16:17:15.462558Z","shell.execute_reply.started":"2024-05-31T16:17:15.438486Z","shell.execute_reply":"2024-05-31T16:17:15.461435Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"                                  dt  AverageTemperature  \\\ncount                           2613         2324.000000   \nmean   1904-10-31 13:13:34.236509696           26.017259   \nmin              1796-01-01 00:00:00           14.607000   \n25%              1850-06-01 00:00:00           21.970500   \n50%              1904-11-01 00:00:00           27.588000   \n75%              1959-04-01 00:00:00           30.038000   \nmax              2013-09-01 00:00:00           33.231000   \nstd                              NaN            4.790453   \n\n       AverageTemperatureUncertainty  \ncount                    2324.000000  \nmean                        0.921346  \nmin                         0.097000  \n25%                         0.377000  \n50%                         0.596500  \n75%                         1.429000  \nmax                         4.886000  \nstd                         0.734068  ","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>dt</th>\n      <th>AverageTemperature</th>\n      <th>AverageTemperatureUncertainty</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2613</td>\n      <td>2324.000000</td>\n      <td>2324.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1904-10-31 13:13:34.236509696</td>\n      <td>26.017259</td>\n      <td>0.921346</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1796-01-01 00:00:00</td>\n      <td>14.607000</td>\n      <td>0.097000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1850-06-01 00:00:00</td>\n      <td>21.970500</td>\n      <td>0.377000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1904-11-01 00:00:00</td>\n      <td>27.588000</td>\n      <td>0.596500</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1959-04-01 00:00:00</td>\n      <td>30.038000</td>\n      <td>1.429000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2013-09-01 00:00:00</td>\n      <td>33.231000</td>\n      <td>4.886000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>NaN</td>\n      <td>4.790453</td>\n      <td>0.734068</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### 2. Perform Outlier Analysis","metadata":{}},{"cell_type":"code","source":"# Calculate IQR for AverageTemperature\nQ1 = df_karachi['AverageTemperature'].quantile(0.25)\nQ3 = df_karachi['AverageTemperature'].quantile(0.75)\nIQR = Q3 - Q1\n\n# Define outliers\nlower_bound = Q1 - 1.5 * IQR\nupper_bound = Q3 + 1.5 * IQR\n\n# Find outliers\noutliers = df_karachi[(df_karachi['AverageTemperature'] < lower_bound) | (df_karachi['AverageTemperature'] > upper_bound)]\noutliers","metadata":{"execution":{"iopub.status.busy":"2024-05-31T16:17:15.464118Z","iopub.execute_input":"2024-05-31T16:17:15.464592Z","iopub.status.idle":"2024-05-31T16:17:15.482394Z","shell.execute_reply.started":"2024-05-31T16:17:15.464555Z","shell.execute_reply":"2024-05-31T16:17:15.481345Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"Empty DataFrame\nColumns: [dt, AverageTemperature, AverageTemperatureUncertainty, City, Country, Latitude, Longitude]\nIndex: []","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>dt</th>\n      <th>AverageTemperature</th>\n      <th>AverageTemperatureUncertainty</th>\n      <th>City</th>\n      <th>Country</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Plot the box plot for 'AverageTemperature'\nplt.figure(figsize=(10, 6))\nplt.boxplot(df_karachi['AverageTemperature'].dropna())  # Drop NaN values if any\nplt.title('Box Plot of Average Temperature in Karachi, Pakistan')\nplt.ylabel('Average Temperature (°C)')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T16:17:15.483714Z","iopub.execute_input":"2024-05-31T16:17:15.484104Z","iopub.status.idle":"2024-05-31T16:17:15.755142Z","shell.execute_reply.started":"2024-05-31T16:17:15.484069Z","shell.execute_reply":"2024-05-31T16:17:15.754071Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"### 3. Scatter Plot","metadata":{}},{"cell_type":"code","source":"# Filter the DataFrame for the specified date range\nstart_date = '2000-01-01'\nend_date = '2013-09-01'\ndf_date_filtered = df_karachi[(df_karachi['dt'] >= start_date) & (df_karachi['dt'] <= end_date)]\n\n# Plot the scatter plot\nplt.figure(figsize=(16, 4))\nplt.scatter(df_date_filtered['dt'], df_date_filtered['AverageTemperature'], alpha=0.5)\nplt.title('Average Temperature in Karachi, Pakistan (2000-01-01 to 2013-09-01)')\nplt.xlabel('Date')\nplt.ylabel('Average Temperature (°C)')\nplt.grid(True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T16:17:15.757818Z","iopub.execute_input":"2024-05-31T16:17:15.758149Z","iopub.status.idle":"2024-05-31T16:17:16.133335Z","shell.execute_reply.started":"2024-05-31T16:17:15.758109Z","shell.execute_reply":"2024-05-31T16:17:16.131975Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x400 with 1 Axes>","image/png":"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