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Commit 2952eb6

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Add Cleaning and Tidying DateTime Data notes to case study
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‎pandas/pandas_Foundations/case_study_sunlight_in_Austin.ipynb

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"## Cleaning and Tidying DateTime Data"
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Wban</th>\n",
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" <th>date</th>\n",
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" <th>Time</th>\n",
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" <th>StationType</th>\n",
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" <th>sky_condition</th>\n",
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" <th>visibility</th>\n",
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" <th>dry_bulb_faren</th>\n",
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" <th>dry_bulb_cel</th>\n",
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" <th>wet_bulb_faren</th>\n",
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" <th>wet_bulb_cel</th>\n",
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" <th>dew_point_faren</th>\n",
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" <th>dew_point_cel</th>\n",
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" <th>relative_humidity</th>\n",
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" <th>wind_speed</th>\n",
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" <th>wind_direction</th>\n",
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" <th>station_pressure</th>\n",
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" <th>sea_level_pressure</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>13904</td>\n",
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" <td>20110101</td>\n",
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" <td>0053</td>\n",
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" <td>12</td>\n",
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" <td>OVC045</td>\n",
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" <td>10.00</td>\n",
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" <td>51</td>\n",
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" <td>10.6</td>\n",
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" <td>38</td>\n",
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" <td>3.1</td>\n",
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" <td>15</td>\n",
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" <td>-9.4</td>\n",
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" <td>24</td>\n",
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" <td>15</td>\n",
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" <td>360</td>\n",
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" <td>29.42</td>\n",
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" <td>29.95</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>13904</td>\n",
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" <td>20110101</td>\n",
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" <td>0153</td>\n",
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" <td>12</td>\n",
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" <td>OVC049</td>\n",
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" <td>10.00</td>\n",
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" <td>51</td>\n",
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" <td>10.6</td>\n",
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" <td>37</td>\n",
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" <td>3.0</td>\n",
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" <td>14</td>\n",
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" <td>-10.0</td>\n",
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" <td>23</td>\n",
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" <td>10</td>\n",
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" <td>340</td>\n",
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" <td>29.49</td>\n",
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" <td>30.01</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>13904</td>\n",
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" <td>20110101</td>\n",
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" <td>0253</td>\n",
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" <td>12</td>\n",
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" <td>OVC060</td>\n",
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" <td>29.49</td>\n",
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" <td>30.01</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>13904</td>\n",
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" <td>20110101</td>\n",
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" <td>0353</td>\n",
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" <td>12</td>\n",
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" <td>OVC065</td>\n",
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" <td>10.0</td>\n",
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" <td>3.1</td>\n",
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" <td>30.03</td>\n",
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" <td>13904</td>\n",
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" <td>020</td>\n",
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" <td>30.04</td>\n",
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" </tr>\n",
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"text/plain": [
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" Wban date Time StationType sky_condition visibility dry_bulb_faren \\\n",
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"0 13904 20110101 0053 12 OVC045 10.00 51 \n",
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"1 13904 20110101 0153 12 OVC049 10.00 51 \n",
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"2 13904 20110101 0253 12 OVC060 10.00 51 \n",
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"3 13904 20110101 0353 12 OVC065 10.00 50 \n",
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"4 13904 20110101 0453 12 BKN070 10.00 50 \n",
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"\n",
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" dry_bulb_cel wet_bulb_faren wet_bulb_cel dew_point_faren dew_point_cel \\\n",
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"0 10.6 38 3.1 15 -9.4 \n",
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"1 10.6 37 3.0 14 -10.0 \n",
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"2 10.6 37 2.9 13 -10.6 \n",
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"3 10.0 38 3.1 17 -8.3 \n",
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"4 10.0 37 2.8 15 -9.4 \n",
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"\n",
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" relative_humidity wind_speed wind_direction station_pressure \\\n",
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"0 24 15 360 29.42 \n",
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"1 23 10 340 29.49 \n",
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"2 22 15 010 29.49 \n",
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"3 27 7 350 29.51 \n",
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"4 25 11 020 29.51 \n",
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"\n",
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" sea_level_pressure \n",
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"0 29.95 \n",
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"1 30.01 \n",
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"2 30.01 \n",
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"3 30.03 \n",
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"4 30.04 "
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Convert the date column to string\n",
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"df_dropped['date'] = df_dropped['date'].astype(str)\n",
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"\n",
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"# Add leading zeroes zeros to the 'Time' column\n",
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"df_dropped['Time'] = df_dropped['Time'].apply(lambda x: '{:0>4}'.format(x))\n",
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"\n",
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"# Concatenate the new date and Time columns\n",
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"date_string = df_dropped['date'] + df_dropped['Time']\n",
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"\n",
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"# Concatenate the new date and Time columns\n",
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"date_times = pd.to_datetime(date_string, format='%Y%m%d%H%M')\n",
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"\n",
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"# Set the index to be the new date_times\n",
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"df_clean = df_dropped.set_index(date_times)\n",
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"\n",
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"df_dropped.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,

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