Advanced Data Analytics Backup

How to read CSV file using script on Jupyter

• pd.call_file('<path>/<file_name>')

• pd.read_csv('<path>/<file_name>')

• pd.see_csv('<path>/<file_name>')

• pd.join_csv('<path>/<file_name>')

How to change column name?

• df.rename(columns={'Class':'PClass'}, inplace=True)

• df.change(columns={'Class':'PClass'}, inplace=True)

• df.add(columns={'Class':'PClass'}, inplace=True)

• df.switch(columns={'Class':'PClass'}, inplace=True)

How to count empty value data in all columns?

• df.null().sum(axis=0)

• df.ifnull().calc(axis=0)

• df.isnull().sum(axis=0)

• df.arenull().sum(axis=0)

Which one is the example of delete the column in dataframe?

• df.throw(['Name','Age'] , axis=1, inplace=True)

• df.delete(['Name','Age'] , axis=1, inplace=True)

• df.drop(['Name','Age'] , axis=1, inplace=True)

• df.kick(['Name','Age'] , axis=1, inplace=True)

How to save clean Dataframe?

• df.to_save(os.path.join('<path>/<file_name>'), index=False)

• df.to_csv(os.path.join('<path>/<file_name>'), index=False)

• df.to_keep(os.path.join('<path>/<file_name>'), index=False)

• df.to_store(os.path.join('<path>/<file_name>'), index=False)

To use histogram, the most important library that you need to import is?

• numpy

• panda

• sklearn

• seaborn

Based on this picture, what the name of this graph?

• Histogram

• Box Chart

• Bar Chart

• Box Plot

Which one is the script used to get Binary Score?

• from sklearn.metrics import binary_score

  binary_score(y_test,y_pred)

• from sklearn.metrics import confusion_matrix

  confusion_matrix(y_test,y_pred)

• from sklearn.metrics import confusion_score

  confusion_score(y_test,y_pred)

• from sklearn.metrics import binary_matrix

  binary_matrix(y_test,y_pred)

All of them are Decision Tree terms EXCEPT :

• Root Node: Represents en6re sample

• Splitting: Dividing a node into sub-nodes

• Leaf Tree: Do get split further or terminal

  node

• Parent and Child Node: Parent divides into

  child sub-nodes

All of this statement are TRUE EXCEPT:-

• Entropy is the measures of purity, preorder and

  uncertainty in a bunch of samples.

• In Decision Tree, entropy is a metric of non-homogeneity of

  the samples and tells how much variance the

  data has.

• pi is the probability of randomly picking an

  element of class i.