10+ Norwich Clinical Services Interview Questions and Answers

Retrieve top 2 cities per state based on max number in third column of pandas dataframe.

• Group the dataframe by state column

• Sort each group by the third column in descending order

• Retrieve the top 2 rows of each group using head(2) function

• Concatenate the resulting dataframes using pd.concat() function

To retain special characters in pandas data, use encoding parameter while reading the data.

• Use encoding parameter while reading the data in pandas

• Specify the encoding type of the data file

• Example: pd.read_csv('filename.csv', encoding='utf-8')

The resultant table will have all the columns from both tables and the rows will be a combination of matching rows.

• The resultant table will have all the columns from both tables

• The rows in the resultant table will be a combination of matching rows

• If the second table has repeated keys, there will be multiple rows with the same key in the resultant table

Use pandas' read_csv() method with appropriate parameters to read large .csv files quickly.

• Use the chunksize parameter to read the file in smaller chunks

• Use the low_memory parameter to optimize memory usage

• Use the dtype parameter to specify data types for columns

• Use the usecols parameter to read only necessary columns

• Use the skiprows parameter to skip unnecessary rows

• Use the nrows parameter to read only a specific number of rows

• Use the na_values parameter to specify values to...read more

my_list[5] retrieves the 6th element of the list.

• Indexing starts from 0 in Python.

• The integer inside the square brackets is the index of the element to retrieve.

• If the index is out of range, an IndexError is raised.

Lambda functions are anonymous functions used for short and simple operations. They are different from regular functions in their syntax and usage.

• Lambda functions are defined without a name and keyword 'lambda' is used to define them.

• They can take any number of arguments but can only have one expression.

• They are commonly used in functional programming and as arguments to higher-order functions.

• Lambda functions are often used for short and simple operations that do not requir...read more

To create dictionaries in Python with repeated keys, use defaultdict from the collections module.

• Import the collections module

• Create a defaultdict object

• Add key-value pairs to the dictionary using the same key multiple times

• Access the values using the key

• Example: from collections import defaultdict; d = defaultdict(list); d['key'].append('value1'); d['key'].append('value2')

Eigenvalues and eigenvectors are linear algebra concepts used in machine learning for dimensionality reduction and feature extraction.

• Eigenvalues represent the scaling factor of the eigenvectors.

• Eigenvectors are the directions along which a linear transformation acts by stretching or compressing.

• In machine learning, eigenvectors are used for principal component analysis (PCA) to reduce the dimensionality of data.

• Eigenvalues and eigenvectors are also used in image processing f...read more

Use vectorized operations, avoid loops, and optimize memory usage.

• Use vectorized operations like apply(), map(), and applymap() instead of loops.

• Avoid using iterrows() and itertuples() as they are slower than vectorized operations.

• Optimize memory usage by using appropriate data types and dropping unnecessary columns.

• Use inplace=True parameter to modify the DataFrame in place instead of creating a copy.

• Use the pd.eval() function to perform arithmetic operations on large DataFr...read more

PCA is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space.

• PCA can be used for feature extraction, data visualization, and noise reduction.

• PCA cannot be used for causal inference or to handle missing data.

• PCA assumes linear relationships between variables and may not work well with non-linear data.

• PCA can be applied to various fields such as finance, image processing, and genetics.

We minimize the loss in logistic regression.

• The goal of logistic regression is to minimize the loss function.

• The loss function measures the difference between predicted and actual values.

• The optimization algorithm tries to find the values of coefficients that minimize the loss function.

• Minimizing the loss function leads to better model performance.

• Examples of loss functions used in logistic regression are cross-entropy and log loss.

One vs Rest is a technique used to extend binary classification to multi-class problems in logistic regression.

• It involves training multiple binary classifiers, one for each class.

• In each classifier, one class is treated as the positive class and the rest as negative.

• The class with the highest probability is predicted as the final output.

• It is also known as one vs all or one vs others.

• Example: In a 3-class problem, we train 3 binary classifiers: class 1 vs rest, class 2 vs re...read more

One vs one classification is a binary classification method where multiple models are trained to classify each pair of classes.

• It is used when there are more than two classes in the dataset.

• It involves training multiple binary classifiers for each pair of classes.

• The final prediction is made by combining the results of all the binary classifiers.

• Example: In a dataset with 5 classes, 10 binary classifiers will be trained for each pair of classes.

VIF stands for Variance Inflation Factor, a measure of multicollinearity in regression analysis.

• VIF is calculated for each predictor variable in a regression model.

• It measures how much the variance of the estimated regression coefficient is increased due to multicollinearity.

• A VIF of 1 indicates no multicollinearity, while a VIF greater than 1 indicates increasing levels of multicollinearity.

• VIF is calculated as 1 / (1 - R^2), where R^2 is the coefficient of determination fro...read more

AIC & BIC are statistical measures used to evaluate the goodness of fit of a linear regression model.

• AIC stands for Akaike Information Criterion and BIC stands for Bayesian Information Criterion.

• Both AIC and BIC are used to compare different models and select the best one.

• AIC penalizes complex models less severely than BIC.

• Lower AIC/BIC values indicate a better fit of the model to the data.

• AIC and BIC can be calculated using the log-likelihood function and the number of param...read more

Generators are functions that allow you to iterate over a sequence of values without creating the entire sequence in memory. Decorators are functions that modify the behavior of other functions.

• Generators use the yield keyword to return values one at a time

• Generators are memory efficient and can handle large datasets

• Decorators are functions that take another function as input and return a modified version of that function

• Decorators can be used to add functionality to existing...read more

Merge and join are used to combine dataframes in pandas.

• Merge is used to combine dataframes based on a common column or index.

• Join is used to combine dataframes based on their index.

• Merge can handle different column names, while join cannot.

• Merge can handle different types of joins (inner, outer, left, right), while join only does inner join by default.