06. Data Visualization/tutorial/03. bar-charts-and-heatmaps.ipynb

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    "Now that you can create your own line charts, it's time to learn about more chart types!  \n",
    "\n",
    "> By the way, if this is your first experience with writing code in Python, you should be _very proud_ of all that you have accomplished so far, because it's never easy to learn a completely new skill!  If you stick with the course, you'll notice that everything will only get easier (while the charts you'll build will get more impressive!), since the code is pretty similar for all of the charts.  Like any skill, coding becomes natural over time, and with repetition.\n",
    "\n",
    "In this tutorial, you'll learn about **bar charts** and **heatmaps**.\n",
    "\n",
    "# Set up the notebook\n",
    "\n",
    "As always, we begin by setting up the coding environment.  (_This code is hidden, but you can un-hide it by clicking on the \"Code\" button immediately below this text, on the right._)"
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      "Setup Complete\n"
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   "source": [
    "\n",
    "import pandas as pd\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "print(\"Setup Complete\")"
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    "# Select a dataset\n",
    "\n",
    "In this tutorial, we'll work with a dataset from the US Department of Transportation that tracks flight delays.\n",
    "\n",
    "Opening this CSV file in Excel shows a row for each month (where `1` = January, `2` = February, etc) and a column for each airline code.\n",
    "\n",
    "![tut2_flight_delay_head](https://i.imgur.com/5nYs9se.png)\n",
    "\n",
    "Each entry shows the average arrival delay (in minutes) for a different airline and month (all in year 2015).  Negative entries denote flights that (_on average_) tended to arrive early.  For instance, the average American Airlines flight (_airline code: **AA**_) in January arrived roughly 7 minutes late, and the average Alaska Airlines flight (_airline code: **AS**_) in April arrived roughly 3 minutes early.\n",
    "\n",
    "# Load the data\n",
    "\n",
    "As before, we load the dataset using the `pd.read_csv` command."
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    "# Path of the file to read\n",
    "flight_filepath = \"../input/flight_delays.csv\"\n",
    "\n",
    "# Read the file into a variable flight_data\n",
    "flight_data = pd.read_csv(flight_filepath, index_col=\"Month\")"
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    "You may notice that the code is slightly shorter than what we used in the previous tutorial.  In this case, since the row labels (from the `'Month'` column) don't correspond to dates, we don't add `parse_dates=True` in the parentheses.  But, we keep the first two pieces of text as before, to provide both: \n",
    "- the filepath for the dataset (in this case, `flight_filepath`), and \n",
    "- the name of the column that will be used to index the rows (in this case, `index_col=\"Month\"`). \n",
    "\n",
    "# Examine the data\n",
    "\n",
    "Since the dataset is small, we can easily print all of its contents.  This is done by writing a single line of code with just the name of the dataset."
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AA</th>\n",
       "      <th>AS</th>\n",
       "      <th>B6</th>\n",
       "      <th>DL</th>\n",
       "      <th>EV</th>\n",
       "      <th>F9</th>\n",
       "      <th>HA</th>\n",
       "      <th>MQ</th>\n",
       "      <th>NK</th>\n",
       "      <th>OO</th>\n",
       "      <th>UA</th>\n",
       "      <th>US</th>\n",
       "      <th>VX</th>\n",
       "      <th>WN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.955843</td>\n",
       "      <td>-0.320888</td>\n",
       "      <td>7.347281</td>\n",
       "      <td>-2.043847</td>\n",
       "      <td>8.537497</td>\n",
       "      <td>18.357238</td>\n",
       "      <td>3.512640</td>\n",
       "      <td>18.164974</td>\n",
       "      <td>11.398054</td>\n",
       "      <td>10.889894</td>\n",
       "      <td>6.352729</td>\n",
       "      <td>3.107457</td>\n",
       "      <td>1.420702</td>\n",
       "      <td>3.389466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.530204</td>\n",
       "      <td>-0.782923</td>\n",
       "      <td>18.657673</td>\n",
       "      <td>5.614745</td>\n",
       "      <td>10.417236</td>\n",
       "      <td>27.424179</td>\n",
       "      <td>6.029967</td>\n",
       "      <td>21.301627</td>\n",
       "      <td>16.474466</td>\n",
       "      <td>9.588895</td>\n",
       "      <td>7.260662</td>\n",
       "      <td>7.114455</td>\n",
       "      <td>7.784410</td>\n",
       "      <td>3.501363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.693587</td>\n",
       "      <td>-0.544731</td>\n",
       "      <td>10.741317</td>\n",
       "      <td>2.077965</td>\n",
       "      <td>6.730101</td>\n",
       "      <td>20.074855</td>\n",
       "      <td>3.468383</td>\n",
       "      <td>11.018418</td>\n",
       "      <td>10.039118</td>\n",
       "      <td>3.181693</td>\n",
       "      <td>4.892212</td>\n",
       "      <td>3.330787</td>\n",
       "      <td>5.348207</td>\n",
       "      <td>3.263341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.931778</td>\n",
       "      <td>-3.009003</td>\n",
       "      <td>2.780105</td>\n",
       "      <td>0.083343</td>\n",
       "      <td>4.821253</td>\n",
       "      <td>12.640440</td>\n",
       "      <td>0.011022</td>\n",
       "      <td>5.131228</td>\n",
       "      <td>8.766224</td>\n",
       "      <td>3.223796</td>\n",
       "      <td>4.376092</td>\n",
       "      <td>2.660290</td>\n",
       "      <td>0.995507</td>\n",
       "      <td>2.996399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.173878</td>\n",
       "      <td>-1.716398</td>\n",
       "      <td>-0.709019</td>\n",
       "      <td>0.149333</td>\n",
       "      <td>7.724290</td>\n",
       "      <td>13.007554</td>\n",
       "      <td>0.826426</td>\n",
       "      <td>5.466790</td>\n",
       "      <td>22.397347</td>\n",
       "      <td>4.141162</td>\n",
       "      <td>6.827695</td>\n",
       "      <td>0.681605</td>\n",
       "      <td>7.102021</td>\n",
       "      <td>5.680777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.191017</td>\n",
       "      <td>-0.220621</td>\n",
       "      <td>5.047155</td>\n",
       "      <td>4.419594</td>\n",
       "      <td>13.952793</td>\n",
       "      <td>19.712951</td>\n",
       "      <td>0.882786</td>\n",
       "      <td>9.639323</td>\n",
       "      <td>35.561501</td>\n",
       "      <td>8.338477</td>\n",
       "      <td>16.932663</td>\n",
       "      <td>5.766296</td>\n",
       "      <td>5.779415</td>\n",
       "      <td>10.743462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.870440</td>\n",
       "      <td>0.377408</td>\n",
       "      <td>5.841454</td>\n",
       "      <td>1.204862</td>\n",
       "      <td>6.926421</td>\n",
       "      <td>14.464543</td>\n",
       "      <td>2.001586</td>\n",
       "      <td>3.980289</td>\n",
       "      <td>14.352382</td>\n",
       "      <td>6.790333</td>\n",
       "      <td>10.262551</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.135773</td>\n",
       "      <td>10.504942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.193907</td>\n",
       "      <td>2.503899</td>\n",
       "      <td>9.280950</td>\n",
       "      <td>0.653114</td>\n",
       "      <td>5.154422</td>\n",
       "      <td>9.175737</td>\n",
       "      <td>7.448029</td>\n",
       "      <td>1.896565</td>\n",
       "      <td>20.519018</td>\n",
       "      <td>5.606689</td>\n",
       "      <td>5.014041</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.106221</td>\n",
       "      <td>5.532108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.432732</td>\n",
       "      <td>-1.813800</td>\n",
       "      <td>3.539154</td>\n",
       "      <td>-3.703377</td>\n",
       "      <td>0.851062</td>\n",
       "      <td>0.978460</td>\n",
       "      <td>3.696915</td>\n",
       "      <td>-2.167268</td>\n",
       "      <td>8.000101</td>\n",
       "      <td>1.530896</td>\n",
       "      <td>-1.794265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070998</td>\n",
       "      <td>-1.336260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.580930</td>\n",
       "      <td>-2.993617</td>\n",
       "      <td>3.676787</td>\n",
       "      <td>-5.011516</td>\n",
       "      <td>2.303760</td>\n",
       "      <td>0.082127</td>\n",
       "      <td>0.467074</td>\n",
       "      <td>-3.735054</td>\n",
       "      <td>6.810736</td>\n",
       "      <td>1.750897</td>\n",
       "      <td>-2.456542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.254278</td>\n",
       "      <td>-0.688851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.772630</td>\n",
       "      <td>-1.916516</td>\n",
       "      <td>1.418299</td>\n",
       "      <td>-3.175414</td>\n",
       "      <td>4.415930</td>\n",
       "      <td>11.164527</td>\n",
       "      <td>-2.719894</td>\n",
       "      <td>0.220061</td>\n",
       "      <td>7.543881</td>\n",
       "      <td>4.925548</td>\n",
       "      <td>0.281064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.116370</td>\n",
       "      <td>0.995684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.149684</td>\n",
       "      <td>-1.846681</td>\n",
       "      <td>13.839290</td>\n",
       "      <td>2.504595</td>\n",
       "      <td>6.685176</td>\n",
       "      <td>9.346221</td>\n",
       "      <td>-1.706475</td>\n",
       "      <td>0.662486</td>\n",
       "      <td>12.733123</td>\n",
       "      <td>10.947612</td>\n",
       "      <td>7.012079</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.498720</td>\n",
       "      <td>6.720893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "             AA        AS         B6        DL         EV         F9  \\\n",
       "Month                                                                  \n",
       "1      6.955843 -0.320888   7.347281 -2.043847   8.537497  18.357238   \n",
       "2      7.530204 -0.782923  18.657673  5.614745  10.417236  27.424179   \n",
       "3      6.693587 -0.544731  10.741317  2.077965   6.730101  20.074855   \n",
       "4      4.931778 -3.009003   2.780105  0.083343   4.821253  12.640440   \n",
       "5      5.173878 -1.716398  -0.709019  0.149333   7.724290  13.007554   \n",
       "6      8.191017 -0.220621   5.047155  4.419594  13.952793  19.712951   \n",
       "7      3.870440  0.377408   5.841454  1.204862   6.926421  14.464543   \n",
       "8      3.193907  2.503899   9.280950  0.653114   5.154422   9.175737   \n",
       "9     -1.432732 -1.813800   3.539154 -3.703377   0.851062   0.978460   \n",
       "10    -0.580930 -2.993617   3.676787 -5.011516   2.303760   0.082127   \n",
       "11     0.772630 -1.916516   1.418299 -3.175414   4.415930  11.164527   \n",
       "12     4.149684 -1.846681  13.839290  2.504595   6.685176   9.346221   \n",
       "\n",
       "             HA         MQ         NK         OO         UA        US  \\\n",
       "Month                                                                   \n",
       "1      3.512640  18.164974  11.398054  10.889894   6.352729  3.107457   \n",
       "2      6.029967  21.301627  16.474466   9.588895   7.260662  7.114455   \n",
       "3      3.468383  11.018418  10.039118   3.181693   4.892212  3.330787   \n",
       "4      0.011022   5.131228   8.766224   3.223796   4.376092  2.660290   \n",
       "5      0.826426   5.466790  22.397347   4.141162   6.827695  0.681605   \n",
       "6      0.882786   9.639323  35.561501   8.338477  16.932663  5.766296   \n",
       "7      2.001586   3.980289  14.352382   6.790333  10.262551       NaN   \n",
       "8      7.448029   1.896565  20.519018   5.606689   5.014041       NaN   \n",
       "9      3.696915  -2.167268   8.000101   1.530896  -1.794265       NaN   \n",
       "10     0.467074  -3.735054   6.810736   1.750897  -2.456542       NaN   \n",
       "11    -2.719894   0.220061   7.543881   4.925548   0.281064       NaN   \n",
       "12    -1.706475   0.662486  12.733123  10.947612   7.012079       NaN   \n",
       "\n",
       "              VX         WN  \n",
       "Month                        \n",
       "1       1.420702   3.389466  \n",
       "2       7.784410   3.501363  \n",
       "3       5.348207   3.263341  \n",
       "4       0.995507   2.996399  \n",
       "5       7.102021   5.680777  \n",
       "6       5.779415  10.743462  \n",
       "7       7.135773  10.504942  \n",
       "8       5.106221   5.532108  \n",
       "9       0.070998  -1.336260  \n",
       "10      2.254278  -0.688851  \n",
       "11      0.116370   0.995684  \n",
       "12     13.498720   6.720893  "
      ]
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   "source": [
    "# Print the data\n",
    "flight_data"
   ]
  },
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   "source": [
    "# Bar chart\n",
    "\n",
    "Say we'd like to create a bar chart showing the average arrival delay for Spirit Airlines (_airline code: **NK**_) flights, by month.  "
   ]
  },
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     "iopub.execute_input": "2021-11-09T00:05:16.062622Z",
     "iopub.status.busy": "2021-11-09T00:05:16.061556Z",
     "iopub.status.idle": "2021-11-09T00:05:16.502462Z",
     "shell.execute_reply": "2021-11-09T00:05:16.503129Z"
    },
    "papermill": {
     "duration": 0.456315,
     "end_time": "2021-11-09T00:05:16.503359",
     "exception": false,
     "start_time": "2021-11-09T00:05:16.047044",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Arrival delay (in minutes)')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N81SjJElSRwxekiRJHTF4SZIkdcTgJUmS1BGDlyRJUkcMXpIkSR0xeEmSJHXE4CVJktQRg5ckSVJHDF6SJEkdMXhJkiR1xOAlSZLUEYOXJElSR9YZdgGSxs9zTvrAsEtYySnP//thlyBJtnhJkiR1ZWDBK8n6Sc5Kcl6Si5Ic1k4/KsnlSc5tb4sGVYMkSdJ8MshTjXcCe1TVrUnWBX6c5LR23t9X1QkD3LckSdK8M7DgVVUF3No+XLe91aD2J0mSNN8NtI9XkrWTnAtcD5xRVWe2s96X5PwkH0nygCnWPTjJkiRLli9fPsgyJUmSOjHQ4FVV91TVImBbYJckOwJvB3YA/gzYHHjrFOseXlWLq2rxggULBlmmJElSJzr5VmNV3QR8D9irqpZV407gc8AuXdQgSZI0bIP8VuOCJJu29zcAng5cmmSrdlqAfYELB1WDJEnSfDLIbzVuBRydZG2agHd8VZ2c5LtJFgABzgX+eoA1SJIkzRuD/Fbj+cDOk0zfY1D7lCRJms8cuV6SJKkjBi9JkqSOGLwkSZI6YvCSJEnqiMFLkiSpIwYvSZKkjhi8JEmSOmLwkiRJ6ojBS5IkqSMGL0mSpI4YvCRJkjpi8JIkSeqIwUuSJKkjBi9JkqSOGLwkSZI6YvCSJEnqiMFLkiSpIwYvSZKkjhi8JEmSOmLwkiRJ6ojBS5IkqSMGL0mSpI4YvCRJkjpi8JIkSeqIwUuSJKkjBi9JkqSOGLwkSZI6YvCSJEnqiMFLkiSpIwYvSZKkjhi8JEmSOmLwkiRJ6ojBS5IkqSMGL0mSpI4MLHglWT/JWUnOS3JRksPa6Q9LcmaSXyU5Lsl6g6pBkiRpPhlki9edwB5VtROwCNgrya7AvwIfqapHAL8DXj3AGiRJkuaNgQWvatzaPly3vRWwB3BCO/1oYN9B1SBJkjSfDLSPV5K1k5wLXA+cAfwauKmq7m4XuQrYZpA1SJIkzRcDDV5VdU9VLQK2BXYBduh33SQHJ1mSZMny5csHVaIkSVJnOvlWY1XdBHwPeBKwaZJ12lnbAldPsc7hVbW4qhYvWLCgizIlSZIGapDfalyQZNP2/gbA04FLaALYi9rFDgK+PqgaJEmS5pN1Zl5kzrYCjk6yNk3AO76qTk5yMfDlJO8Ffg4cMcAaJEmS5o2BBa+qOh/YeZLpl9H095IkSVqjOHK9JElSRwxekiRJHTF4SZIkdcTgJUmS1BGDlyRJUkcMXpIkSR0xeEmSJHXE4CVJktQRg5ckSVJHDF6SJEkdMXhJkiR1xOAlSZLUEYOXJElSRwxekiRJHTF4SZIkdcTgJUmS1BGDlyRJUkcMXpIkSR0xeEmSJHXE4CVJktQRg5ckSVJHDF6SJEkdMXhJkiR1xOAlSZLUEYOXJElSRwxekiRJHTF4SZIkdcTgJUmS1BGDlyRJUkcMXpIkSR0xeEmSJHXE4CVJktSRdfpZKMlmwNbAHcDSqrp3oFVJkiSNoSmDV5I/Al4P7AesBywH1ge2TPJT4P9W1fc6qVKSJGkMTNfidQJwDPCUqrqpd0aSJwAHJPnjqjpigPVJkiSNjSmDV1U9fZp5ZwNnT7fhJNvRBLctgQIOr6qPJXk38FqaFjSAd1TVqbOsW5IkaeTM2McryZOBc6vqtiSvAB4PfKyqrphh1buBQ6vqnCQbA2cnOaOd95Gq+uAqVS5JkjRi+vlW4yeB25PsBBwK/JqmJWtaVbWsqs5p798CXAJsswq1SpIkjbR+gtfdVVXAPsDHq+oTwMaz2UmShcDOwJntpDckOT/Jke03Jidb5+AkS5IsWb58+WSLSJIkjZR+gtctSd4OHACckmQtYN1+d5DkgcCJwCFVdTNNC9rDgUXAMuBDk61XVYdX1eKqWrxgwYJ+dydJkjRv9RO8XgrcCfxVVV0LbAt8oJ+NJ1mXJnR9saq+ClBV11XVPe1YYJ8BdplT5ZIkSSNmxuDVhq0TgQe0k24ATpppvSQBjgAuqaoP90zfqmex5wMXzqZgSZKkUdXPtxpfCxwMbE5zinAb4FPAnjOs+mSa05MXJDm3nfYOYL8ki2iGmFgKvG4OdUuSJI2cfi4Z9Hqa04FnAlTVL5M8eKaVqurHQCaZ5ZhdkiRpjdRPH687q+r3Kx4kWYemtUqSJEmz0E/w+kGSdwAbJHk68BXg3wdbliRJ0vjpJ3i9jebyPhfQ9Mc6tareOdCqJEmSxlA/fbzeWFUfoxn6AYAkb26nSZIkqU/9tHgdNMm0V67mOiRJksbelC1eSfYDXg48LMk3emZtDNw46MIkSZLGzXSnGv+T5pI+W3D/y/rcApw/yKIkSZLG0ZTBq6quAK4AntRdOZIkSeOrn5Hrb+G+cbvWo7lA9m1VtckgC5MkSRo3Mwavqtp4xf32+ov7ALsOsihJkqRx1M+3Gv+gGl8DnjmYciRJksZXP6caX9DzcC1gMfA/A6tIkiRpTPUzgOpze+7fDSylOd0oSZKkWeinj9eruihEkiRp3PVzqnEB8FpgYe/yVfVXgytLkiRp/PRzqvHrwI+AbwP3DLYcSZKk8dVP8Nqwqt468EokSZLGXD/B6+Qkz66qUwdejaT7edVJew27hJV87vnfHHYJkjSy+hnH68004euOJDcnuSXJzYMuTJIkadzMauR6SZIkzd2UwSvJDlV1aZLHTza/qs4ZXFmSJEnjZ7oWr7cABwMfmmReAXsMpCJJkqQxNWXwqqqD259P7a4cSZKk8dXPAKprA89h5QFUPzy4siRJksZPP8NJ/DvNRbEvAO4dbDmSJEnjq5/gtW1VPW7glUiSJI25fsbxOi3JMwZeiSRJ0pjrp8Xrp8BJSdYC7gICVFVtMtDKJEmSxkw/wevDwJOAC6qqBlyPJEnS2OrnVOOVwIWGLkmSpFXTT4vXZcD3k5wG3LliosNJSJIkzU4/wevy9rZee5MkSdIc9HOR7MO6KESSJGnc9dPHS5IkSatBP6caJUnz3L4nfGfYJazkay/ac9glSPPOwFq8kmyX5HtJLk5yUZI3t9M3T3JGkl+2PzcbVA2SJEnzST8XyV4AvJaVL5L9VzOsejdwaFWdk2Rj4OwkZwCvBL5TVe9P8jbgbcBb51a+JEnS6OjnVOPXgR8B3wbu6XfDVbUMWNbevyXJJcA2wD7A7u1iRwPfx+AlSZLWAP0Erw2rapWCUZKFwM7AmcCWbSgDuBbYcop1DgYOBth+++1XZfeSJEnzQj99vE5O8uy57iDJA4ETgUOq6ubeee1o+JOOiF9Vh1fV4qpavGDBgrnuXpIkad7oJ3i9mSZ83ZHk5iS3JLl5xrWAJOvShK4vVtVX28nXJdmqnb8VcP1cCpckSRo1Mwavqtq4qtaqqg2qapP28SYzrZckwBHAJRMuL/QN4KD2/kE0fcgkSZLG3pR9vJLsUFWXJnn8ZPOr6pwZtv1k4ADggiTnttPeAbwfOD7Jq4ErgJfMumpJkqQRNF3n+rfQdG7/0CTzCthjug1X1Y+BTDHbUfUkSdIaZ8rgVVUHtz+f2l05kiRJ42vKPl5JdptuxSSbJNlx9ZckSZI0nqY71fjCJP8H+CZwNrAcWB94BPBU4KHAoQOvUJIkaUxMd6rx75JsDrwQeDGwFXAHcAnw6bYPlyRJkvo07cj1VXUj8Jn2JkmSpFXQzwCqkiRJWg0MXpIkSR0xeEmSJHVkxuCV5Owkr0+yWRcFSZIkjat+WrxeCmwN/CzJl5M8s70OoyRJkmahn4tk/6qq3gk8CjgWOBK4Islh7XATkiRJ6kNffbySPI7mmo0fAE6kGdfrZuC7gytNkiRpvEw7jhc0fbyAm4AjgLdV1Z3trDOTPHmAtUmSJI2VGYMX8OKqumyyGVX1gtVcjyRJ0tiaMXhV1WVJngP8Cc21GldMf88gC5MkSRo3/Qwn8Smabza+EQhN/66HDrguSZKksdNP5/o/r6oDgd9V1WHAk2i+4ShJkqRZ6Cd43dH+vD3J1sBdwFaDK0mSJGk89dO5/uQkm9IMJXEOUMBnB1mUJEnSOOqnc/2/tHdPTHIysH5V/fdgy5IkSRo/UwavJFMOFZGEqvrqYEqSJEkaT9O1eD13mnkFGLwkSZJmYcrgVVWv6rIQSZKkcdfPOF5bJjkiyWnt48cmefXgS5MkSRov/QwncRTwLWDr9vF/AYcMqB5JkqSx1c9wEltU1fFJ3g5QVXcnuWfAdY2t3/zbi4Zdwkq2f9MJwy5BkqQ1Qj8tXrcleRBNh3qS7Ao4nIQkSdIs9dPi9RbgG8DDk/wHsACYf802kiRJ81w/A6iek+QvgUfTXCT7F1V118ArkyRJGjNzGUD1UQ6gKkmSNHv9DKD6YODPge+2j58K/CcOoCpJkjQrMw6gmuR04LFVtax9vBXNEBOSJEmahX6+1bjditDVug7YfkD1SJIkja1+vtX4nSTfAr7UPn4p8O3BlSRJkjSeZmzxqqo3AJ8Cdmpvh1fVG2daL8mRSa5PcmHPtHcnuTrJue3t2atSvCRJ0ijpp8WLqjoJOGmW2z4K+DhwzITpH6mqD85yW5IkSSOvnz5ec1JVPwRuHNT2JUmSRs3Agtc03pDk/PZU5GZD2L8kSdJQdB28Pgk8HFgELAM+NNWCSQ5OsiTJkuXLl3dUniRJ0uBMN3L9BbQXxp44C6iqetxsd1ZV1/Vs/zPAydMsezhwOMDixYsnq0OSJGmkTNe5fu/VvbMkW/WMCfZ84MLplpckSRon041cf8WqbDjJl4DdgS2SXAW8C9g9ySKalrSlwOtWZR+SJEmjZMbhJJLsCvz/wGOA9YC1gduqapPp1quq/SaZfMRcipQkSRoH/Yzj9XHgZcBXgMXAgcCjBlmUJA3L3id8cdglrOTkF+0/7BIkrSZ9fauxqn4FrF1V91TV54C9BluWJEnS+Omnxev2JOsB5yb5PzTDQAxj/C9JkqSR1k+AOqBd7g3AbcB2wAsHWZQkSdI46qfF6wnAKVV1M3DYgOuRJEkaW/20eD0X+K8kn0+yd5K+LqwtSZKk+5sxeFXVq4BH0HyrcT/g10k+O+jCJEmSxk1frVdVdVeS02gGPt0A2Bd4zQDrkiRJGjsztngleVaSo4Bf0nSq/yzwkAHXJUmSNHb6afE6EDgOeF1V3TngeiRJksbWjMFrikv/SJIkaZamDF5JflxVuyW5haZv1x9mATXTtRolSZJ0f1MGr6rarf25cXflSJIkja9pO9cnWTvJpV0VI0mSNM6mDV5VdQ/wiyTbd1SPJEnS2OrnW42bARclOYvmWo0AVNXzBlaVJEnSBNd97CfDLmElW775SbNavp/g9U9zK0WSJEm9pg1eSdYGPl1VO3RUjyRJ0tiyj5ckSVJH7OMlSZLUEft4SZIkdaSfSwb9oPdxkt2A/YAfTL6GJEmSJtNPixdJdgZeDrwYuBw4cZBFSZIkjaPprtX4KJqWrf2AG4DjgFTVUzuqTZIkaaxM1+J1KfAjYO+q+hVAkr/rpCpJkqQxNN1wEi8AlgHfS/KZJHsC6aYsSZKk8TNli1dVfQ34WpKNgH2AQ4AHJ/kkcFJVnd5JhVNY/skvDHP3k1rwN68YdgmSJGkem3YAVYCquq2qjq2q5wLbAj8H3jrwyiRJksbMjMGrV1X9rqoOr6o9B1WQJEnSuJpV8JIkSdLcGbwkSZI6YvCSJEnqiMFLkiSpIwYvSZKkjhi8JEmSOjKw4JXkyCTXJ7mwZ9rmSc5I8sv252aD2r8kSdJ8M8gWr6OAvSZMexvwnap6JPCd9rEkSdIaYWDBq6p+CNw4YfI+wNHt/aOBfQe1f0mSpPlmyms1DsiWVbWsvX8tsOVUCyY5GDgYYPvtt++gNM3kW0c8e9glrOSZrz512CVIktS3oXWur6oCapr5h1fV4qpavGDBgg4rkyRJGoyug9d1SbYCaH9e3/H+JUmShqbr4PUN4KD2/kHA1zvevyRJ0tAMcjiJLwE/AR6d5KokrwbeDzw9yS+Bp7WPJUmS1ggD61xfVftNMWvPQe1TkiRpPnPkekmSpI4YvCRJkjpi8JIkSeqIwUuSJKkjBi9JkqSOdH3JIKlzn/78M4ddwkped8C3hl2CJGkIbPGSJEnqiMFLkiSpIwYvSZKkjhi8JEmSOmLwkiRJ6ojBS5IkqSMOJyFJGqo3nXTlsEtYyb89f7thl6AxZYuXJElSRwxekiRJHTF4SZIkdcQ+XpIkrWGWfvTaYZewkoWHPGTYJXTCFi9JkqSOGLwkSZI6YvCSJEnqiMFLkiSpIwYvSZKkjvitRkmS5uC0424YdgkredZLtxh2CZqBLV6SJEkdMXhJkiR1xOAlSZLUEYOXJElSRwxekiRJHTF4SZIkdcTgJUmS1BGDlyRJUkcMXpIkSR0xeEmSJHXE4CVJktQRg5ckSVJHhnKR7CRLgVuAe4C7q2rxMOqQJEnq0lCCV+upVTX/Lu0uSZI0IJ5qlCRJ6siwglcBpyc5O8nBky2Q5OAkS5IsWb58ecflSZIkrX7DCl67VdXjgWcBr0/yFxMXqKrDq2pxVS1esGBB9xVKkiStZkMJXlV1dfvzeuAkYJdh1CFJktSlzoNXko2SbLziPvAM4MKu65AkSeraML7VuCVwUpIV+z+2qr45hDokSZI61XnwqqrLgJ263q8kSdKwOZyEJElSRwxekiRJHTF4SZIkdcTgJUmS1BGDlyRJUkcMXpIkSR0xeEmSJHXE4CVJktQRg5ckSVJHDF6SJEkdMXhJkiR1xOAlSZLUEYOXJElSRwxekiRJHTF4SZIkdcTgJUmS1BGDlyRJUkcMXpIkSR0xeEmSJHXE4CVJktQRg5ckSVJHDF6SJEkdMXhJkiR1xOAlSZLUEYOXJElSRwxekiRJHTF4SZIkdcTgJUmS1BGDlyRJUkcMXpIkSR0xeEmSJHXE4CVJktQRg5ckSVJHDF6SJEkdGUrwSrJXkl8k+VWStw2jBkmSpK51HrySrA18AngW8FhgvySP7boOSZKkrg2jxWsX4FdVdVlV/R74MrDPEOqQJEnq1DCC1zbAlT2Pr2qnSZIkjbVUVbc7TF4E7FVVr2kfHwA8sareMGG5g4GD24ePBn4xoJK2AG4Y0LYHbVRrH9W6YXRrH9W6YXRrH9W6YXRrH9W6YXRrH9W6YbC1P7SqFkw2Y50B7XA6VwPb9Tzetp12P1V1OHD4oItJsqSqFg96P4MwqrWPat0wurWPat0wurWPat0wurWPat0wurWPat0wvNqHcarxZ8AjkzwsyXrAy4BvDKEOSZKkTnXe4lVVdyd5A/AtYG3gyKq6qOs6JEmSujaMU41U1anAqcPY9yQGfjpzgEa19lGtG0a39lGtG0a39lGtG0a39lGtG0a39lGtG4ZUe+ed6yVJktZUXjJIkiSpI2ts8EpyZJLrk1w47FpmI8l2Sb6X5OIkFyV587Br6leS9ZOcleS8tvbDhl3TbCRZO8nPk5w87FpmI8nSJBckOTfJkmHX068kmyY5IcmlSS5J8qRh19SPJI9uX+sVt5uTHDLsuvqR5O/av80Lk3wpyfrDrqlfSd7c1n3RfH+9J/v8SbJ5kjOS/LL9udkwa5zMFHW/uH3N700yL7/dOEXdH2jfW85PclKSTbuqZ40NXsBRwF7DLmIO7gYOrarHArsCrx+hSy7dCexRVTsBi4C9kuw63JJm5c3AJcMuYo6eWlWLRuxr3x8DvllVOwA7MSKvfVX9on2tFwFPAG4HThpuVTNLsg3wJmBxVe1I8+Wnlw23qv4k2RF4Lc2VUXYC9k7yiOFWNa2jWPnz523Ad6rqkcB32sfzzVGsXPeFwAuAH3ZeTf+OYuW6zwB2rKrHAf8FvL2rYtbY4FVVPwRuHHYds1VVy6rqnPb+LTQfRiMx8n81bm0frtveRqKTYZJtgecAnx12LWuCJH8E/AVwBEBV/b6qbhpqUXOzJ/Drqrpi2IX0aR1ggyTrABsC1wy5nn49Bjizqm6vqruBH9CEgXlpis+ffYCj2/tHA/t2WVM/Jqu7qi6pqkENcL5aTFH36e2xAvBTmjFFO7HGBq9xkGQhsDNw5pBL6Vt7uu5c4HrgjKoaldo/CvwDcO+Q65iLAk5PcnZ7RYhR8DBgOfC59vTuZ5NsNOyi5uBlwJeGXUQ/qupq4IPAb4BlwH9X1enDrapvFwJPSfKgJBsCz+b+A3WPgi2rall7/1pgy2EWs4b5K+C0rnZm8BpRSR4InAgcUlU3D7ueflXVPe0pmG2BXdpTBPNakr2B66vq7GHXMke7VdXjgWfRnJr+i2EX1Id1gMcDn6yqnYHbmJ+nXqbUDhD9POArw66lH22fon1oQu/WwEZJXjHcqvpTVZcA/wqcDnwTOBe4Z5g1rYpqhhsYibMBoy7JO2m68Hyxq30avEZQknVpQtcXq+qrw65nLtrTRt9jNPrZPRl4XpKlwJeBPZJ8Ybgl9a9tyaCqrqfpa7TLcCvqy1XAVT0toifQBLFR8izgnKq6btiF9OlpwOVVtbyq7gK+Cvz5kGvqW1UdUVVPqKq/AH5H029nlFyXZCuA9uf1Q65n7CV5JbA3sH91OLaWwWvEJAlNv5dLqurDw65nNpIsWPHNkSQbAE8HLh1qUX2oqrdX1bZVtZDm1NF3q2okWgKSbJRk4xX3gWfQnJaZ16rqWuDKJI9uJ+0JXDzEkuZiP0bkNGPrN8CuSTZs32f2ZES+0ACQ5MHtz+1p+ncdO9yKZu0bwEHt/YOArw+xlrGXZC+a7iPPq6rbu9z3Ghu8knwJ+Anw6CRXJXn1sGvq05OBA2haXVZ8Xf3Zwy6qT1sB30tyPs01O8+oqpEammEEbQn8OMl5wFnAKVX1zSHX1K83Al9sj5dFwP8abjn9a0Pu02lajUZC27p4AnAOcAHN58MojUp+YpKLgX8HXj+fv4wxxefP+4GnJ/klTevj+4dZ42QmqzvJ85NcBTwJOCXJt4Zb5cqmeL0/DmwMnNF+jn6qs3ocuV6SJKkba2yLlyRJUtcMXpIkSR0xeEmSJHXE4CVJktQRg5ckSVJHDF6SRlqS6h3QNsk6SZYnmdNQJUk2TfK3PY93n+u2JGkig5ekUXcbsGM7KC8042ddvQrb2xT425kWkqS5MHhJGgenAs9p799vxPgkmyf5WpLzk/w0yePa6e9OcmSS7ye5LMmb2lXeDzy8HVTxA+20ByY5IcmlSb7YjuwuSbNm8JI0Dr4MvCzJ+sDjgDN75h0G/LyqHge8AzimZ94OwDNprl/5rvY6qG8Dfl1Vi6rq79vldgYOAR4L/DHNFSQkadYMXpJGXlWdDyykae06dcLs3YDPt8t9F3hQkk3aeadU1Z1VdQPNRYm3nGIXZ1XVVVV1L3Buuy9JmrV1hl2AJK0m3wA+COwOPKjPde7suX8PU78n9rucJE3LFi9J4+JI4LCqumDC9B8B+0PzDUXghqq6eZrt3EJz8VxJWu38r03SWKiqq4B/m2TWu4Ejk5wP3A4cNMN2fpvkP5JcCJwGnLK6a5W05kpVDbsGSZKkNYKnGiVJkjpi8JIkSeqIwUuSJKkjBi9JkqSOGLwkSZI6YvCSJEnqiMFLkiSpIwYvSZKkjvw/LFWDRYdIO0gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Add title\n",
    "plt.title(\"Average Arrival Delay for Spirit Airlines Flights, by Month\")\n",
    "\n",
    "# Bar chart showing average arrival delay for Spirit Airlines flights by month\n",
    "sns.barplot(x=flight_data.index, y=flight_data['NK'])\n",
    "\n",
    "# Add label for vertical axis\n",
    "plt.ylabel(\"Arrival delay (in minutes)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf9d5f3f",
   "metadata": {
    "papermill": {
     "duration": 0.011962,
     "end_time": "2021-11-09T00:05:16.527715",
     "exception": false,
     "start_time": "2021-11-09T00:05:16.515753",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The commands for customizing the text (title and vertical axis label) and size of the figure are familiar from the previous tutorial.  The code that creates the bar chart is new:\n",
    "\n",
    "```python\n",
    "# Bar chart showing average arrival delay for Spirit Airlines flights by month\n",
    "sns.barplot(x=flight_data.index, y=flight_data['NK'])\n",
    "```\n",
    "It has three main components:\n",
    "- `sns.barplot` - This tells the notebook that we want to create a bar chart.\n",
    " - _Remember that `sns` refers to the [seaborn](https://seaborn.pydata.org/) package, and all of the commands that you use to create charts in this course will start with this prefix._\n",
    "- `x=flight_data.index` - This determines what to use on the horizontal axis.  In this case, we have selected the column that **_index_**es the rows (in this case, the column containing the months).\n",
    "- `y=flight_data['NK']` - This sets the column in the data that will be used to determine the height of each bar.  In this case, we select the `'NK'` column.\n",
    "\n",
    "> **Important Note**: You must select the indexing column with `flight_data.index`, and it is not possible to use `flight_data['Month']` (_which will return an error_).  This is because when we loaded the dataset, the `\"Month\"` column was used to index the rows.  **We always have to use this special notation to select the indexing column.**\n",
    "\n",
    "# Heatmap\n",
    "\n",
    "We have one more plot type to learn about: **heatmaps**! \n",
    "\n",
    "In the code cell below, we create a heatmap to quickly visualize patterns in `flight_data`.  Each cell is color-coded according to its corresponding value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f02ed3c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-09T00:05:16.615306Z",
     "iopub.status.busy": "2021-11-09T00:05:16.597749Z",
     "iopub.status.idle": "2021-11-09T00:05:17.700469Z",
     "shell.execute_reply": "2021-11-09T00:05:17.700995Z"
    },
    "papermill": {
     "duration": 1.161493,
     "end_time": "2021-11-09T00:05:17.701317",
     "exception": false,
     "start_time": "2021-11-09T00:05:16.539824",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 42.0, 'Airline')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(14,7))\n",
    "\n",
    "# Add title\n",
    "plt.title(\"Average Arrival Delay for Each Airline, by Month\")\n",
    "\n",
    "# Heatmap showing average arrival delay for each airline by month\n",
    "sns.heatmap(data=flight_data, annot=True)\n",
    "\n",
    "# Add label for horizontal axis\n",
    "plt.xlabel(\"Airline\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83531dfe",
   "metadata": {
    "papermill": {
     "duration": 0.013274,
     "end_time": "2021-11-09T00:05:17.728414",
     "exception": false,
     "start_time": "2021-11-09T00:05:17.715140",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The relevant code to create the heatmap is as follows:\n",
    "```python\n",
    "# Heatmap showing average arrival delay for each airline by month\n",
    "sns.heatmap(data=flight_data, annot=True)\n",
    "```\n",
    "This code has three main components:\n",
    "- `sns.heatmap` - This tells the notebook that we want to create a heatmap.\n",
    "- `data=flight_data` - This tells the notebook to use all of the entries in `flight_data` to create the heatmap.\n",
    "- `annot=True` - This ensures that the values for each cell appear on the chart.  (_Leaving this out removes the numbers from each of the cells!_)\n",
    "\n",
    "_What patterns can you detect in the table?  For instance, if you look closely, the months toward the end of the year (especially months 9-11) appear relatively dark for all airlines.  This suggests that airlines are better (on average) at keeping schedule during these months!_  \n",
    "\n",
    "# What's next?\n",
    "\n",
    "Create your own visualizations with a **[coding exercise](https://www.kaggle.com/kernels/fork/2951537)**!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eba844de",
   "metadata": {
    "papermill": {
     "duration": 0.013206,
     "end_time": "2021-11-09T00:05:17.755275",
     "exception": false,
     "start_time": "2021-11-09T00:05:17.742069",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/data-visualization/discussion) to chat with other learners.*"
   ]
  }
 ],
 "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": "3.7.10"
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  "papermill": {
   "default_parameters": {},
   "duration": 13.659399,
   "end_time": "2021-11-09T00:05:18.480820",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-11-09T00:05:04.821421",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}