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# From https://stackoverflow.com/questions/23586510/return-multiple-columns-from-pandas-apply

def sizes(s):    
    return locale.format("%.1f", s / 1024.0, grouping=True) + ' KB', \
        locale.format("%.1f", s / 1024.0 ** 2, grouping=True) + ' MB', \
        locale.format("%.1f", s / 1024.0 ** 3, grouping=True) + ' GB'
df_test['size_kb'],  df_test['size_mb'], df_test['size_gb'] = zip(*df_test['size'].apply(sizes))

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pivot.columns

MultiIndex([('mean', 'is_suitable'),
            ('size', 'is_suitable')],
           )

pivot.columns.map('_'.join)

Index(['mean_is_suitable', 'size_is_suitable'], dtype='object')

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def group_others(serie: pd.Series, 
                  min_threshold: int) -> pd.Series:
  """
  This function finds categorical values with little representation
  and group them under the category "OTHERS" to mitigate the curse
  of dimensionality, thus avoiding overfitting
  """
                 
  condition = (serie.value_counts() < min_threshold).values
  other_group = list(serie.value_counts()[condition].index)

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import numpy as np
from keras.models import Sequential
from keras.layers.core import Activation, Dense
from keras.optimizers import SGD

X = np.array([[0,0],[0,1],[1,0],[1,1]], "float32")
y = np.array([[0],[1],[1],[0]], "float32")

model = Sequential()
model.add(Dense(2, input_dim=2, activation='sigmoid'))

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import pylab as plt

plt.plot([1,2,3,10], [1,2,3,4])
%matplot plt # Include this in the same cell as the plot

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def diversity_percentage(df, columns):
    """
    This function returns the number of different elements in each column as a percentage of the total elements in the group.
    A low value indicates there are many repeated elements.
    Example 1: a value of 0 indicates all values are the same.
    Example 2: a value of 100 indicates all values are different.
    """
    diversity = dict()

    for col in columns:

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def plot_nulls(dataframe):
    
    def null_perc(dataframe):
        return 100*dataframe.isnull().sum()/len(dataframe)
    
    nulls = null_perc(dataframe)
    plt.figure(1, figsize=(5,20)) # Customize this if needed
    ax = sns.barplot(x=nulls, y=list(range(len(nulls))), orient='h', color="blue")
    _ = plt.yticks(plt.yticks()[0], nulls.index)
    ax.xaxis.set_ticks_position('top')

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1. Clone your fork:

git clone git@github.com:YOUR-USERNAME/YOUR-FORKED-REPO.git

2. Add remote from original repository in your forked repository:

cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream

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##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

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from IPython.core.debugger import Tracer; 


# Place this call wherever you want to start debugging
Tracer()() 

"""
Some PDB Debuger commands:
n(ext) line and run this one
c(ontinue) running until next breakpoint