Python Data Analysis Interview Questions
Top Python Data Analysis Interview Questions
Python Data Analysis Interview Questions
In a data analysis interview for Python, candidates may be asked about various topics such as data manipulation with pandas, visualization with libraries like Matplotlib or Seaborn, statistical analysis, machine learning concepts using libraries like scikit-learn, and data cleaning techniques. Questions may range from explaining how to perform specific tasks using Python libraries to discussing the candidate's approach to solving real-world data analysis problems. It is important for candidates to showcase their understanding of Python programming, data manipulation skills, and ability to derive insights from data during these interviews.
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1 - What is the difference between loc and iloc in Pandas?
Answer: The loc function is label based, meaning that you have to specify the name of the rows and columns that you want to filter out. On the other hand, iloc is integer index based, so you have to specify rows and columns by their integer index.
2) How do you handle missing values in a Pandas DataFrame?
Answer: Missing values can be handled in Pandas by using functions such as dropna() to remove rows or columns with missing values, fillna() to fill in missing values with a specified value, or interpolate() to interpolate missing values based on existing data.
3) Explain the concept of groupby in Pandas.
Answer: The groupby function in Pandas is used to split the data into groups based on some criteria, perform some operations on these groups, and then combine the results. It is commonly used for aggregating data, such as calculating sums or means for different groups within a dataset.
4) What are some common data types in Python used for data analysis?
Answer: Some common data types used in Python for data analysis include integers, floating point numbers, strings, lists, dictionaries, and Pandas Series and DataFrames for tabular data.
5) How can you merge two DataFrames in Pandas?
Answer: DataFrames in Pandas can be merged using the merge function, which allows you to combine two DataFrames based on a common column or index. You can specify the type of join (inner, outer, left, right) and the columns to join on.
6) Describe the process of feature scaling in machine learning.
Answer: Feature scaling is a technique used in machine learning to standardize the range of independent variables or features of data. Common methods include normalization (scaling features to a range between 0 and 1) and standardization (scaling features to have a mean of 0 and a standard deviation of 1).
7) How do you evaluate the performance of a machine learning model?
Answer: The performance of a machine learning model can be evaluated using metrics such as accuracy, precision, recall, F1 score, ROC AUC, and mean squared error (MSE), depending on the type of problem (classification or regression) you are working on. Cross validation techniques can also be used to assess model performance on unseen data.
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