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with ProgressBar():
  print(nyc_data_raw.drop('total_amount', axis=1).head())

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...
# load all datasets
(azData, azLabels) = load_az_dataset(args["az"])
(digitsData, digitsLabels) = load_zero_nine_dataset()

# the MNIST dataset occupies the labels 0-9, so let's add 10 to every A-Z label to ensure the A-Z characters are not incorrectly labeled as digits
azLabels += 10

# stack the A-Z data and labels with the MNIST digits data and labels
data = np.vstack([azData, digitsData])

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def load_zero_nine_dataset():
    # load the MNIST dataset and stack the training data and testing
    # data together (we'll create our own training and testing splits
    # later in the project)
    ((trainData, trainLabels), (testData, testLabels)) = mnist.load_data()
    data = np.vstack([trainData, testData])
    labels = np.hstack([trainLabels, testLabels])
    # return a 2-tuple of the MNIST data and labels
    return (data, labels)

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def load_az_dataset(dataset_path):
    # initialize the list of data and labels
    data = []
    labels = []

    # loop over the rows of the A-Z handwritten digit dataset
    for row in open(dataset_path):
        # parse the label and image from the row
        row = row.split(",")
        label = int(row[0])

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# TensorFlow and tf.keras
import tensorflow as tf

from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt

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/*
* s, d, e represents three pegs (source, destination and extra).
* n is number of discs (All initially in s)
*/

resolver_tower_of_hanoi = function (s, d, e, n) {

    // terminating condition
    if (n <= 0) {
        return

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 look_for_key = function(box) {
     for (item in box) {
         if (item.is_a_key()) {
             print("found the key")
         } else {
             look_for_key(item)
         }
     }
 }

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// ✗ avoid
function animal () { ... }  

// ✓ ok
function Animal () { ... }    

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// ✓ ok
if (flag) {
  // ...
} else {
  // ...
}

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// ✗ avoid
if (obj.prop) { .. }      
if (obj.prop !== undefined) { ... }

// ✓ ok
if (Object.prototype.hasOwnProperty.call(obj, 'prop')) { ... }