Featured 39 Python Data Analytics Interview Questions Early Preparation for Golden 3 and Silver 4

Python Data Analytics Interview Questions Quiz Tips

In this era of challenges and opportunities, mastering Python data analysis skills is undoubtedly going to be a favorable plus for you. Whether you are a novice who has just stepped into the job market or a professional who has been in the data field for many years, you can't get away from the skillful application of Python.

To help you better tackle the challenges of data analytics, I will be sharing 39 Python Data Analytics Interview Questions in this article, covering a wide range of topics, from basics to advanced skills without missing a beat.

If you want to successfully pass your Python Data Analytics interview, then don't miss this article. Keep reading, or bookmark and share it with your friends and let's get started!

Question: How do I read data from a CSV file using Python?

Answer: To read data from a CSV file, you can use thepandas Library. The commonly used ones areread_csv function. Example:

import pandas as pd
data = pd.read_csv('')

Question: Explain the difference between a list and a NumPy array in Python.

Answer: While lists are the basic Python data structure, NumPy arrays are specialized for numerical operations.NumPy arrays are homogeneous and support vectorized operations, making them more efficient in numerical calculations.

Question: How to handle missing values in Pandas data frame?

Answer: In Pandas, thedropna() respond in singingfillna() method is often used to handle missing values. Example

()  # Drop rows with missing values
(value)  # Fill missing values with a specified value

Question: Explain the usage of lambda function in Python.

Answers: lambdafunction is a function that uses thelambda keyword creates anonymous functions. They are used for short-term operations and are usually associated withmap maybefilter and other functions together. Example

square = lambda x: x**2

Question: How do I install external libraries in Python?

Answer: It is possible to usepip tool to install external libraries. For example

pip install pandas

Question: Describe the uses of the NumPy and Pandas libraries in Python.

Answers: NumPyUsed for numerical operations and providing support for arrays and matrices, Pandas is a data manipulation and analysis library that introduces data structures such as DataFrames to make it easier to work with and analyze tabular data.

Question: How to work with categorized data in Pandas dataframe?

Answer: utilizationget_dummies()function converts categorical variables to dummy/indicator variables. Example

pd.get_dummies(df, columns=['Category'])

Question: What does the matplotlib library in Python do?

Answer: Matplotlibis a Python plotting library. It provides a variety of chart types for visualizing data, such as line charts, bar charts, and scatter plots.

Question: Explain the usage of groupby function in Pandas.

Answer: groupbyFunctions are used to group data based on certain criteria and apply a function independently to each grouping. Example:

grouped_data = ('Category').mean()

Question: How do you handle outliers in a dataset?

Answer: You can handle outliers by filtering them or converting them using statistical methods. For example, you can use interquartile spacing (IQR) to identify and remove outliers.

Question: What does the "Seaborn" library in Python do?

Answer: "Seaborn" is a Matplotlib-based library for visualizing statistical data. It provides a high-level interface for drawing attractive and informative statistical graphics.

Question: Explain the difference between shallow copy and deep copy in Python.

Answer: Shallow copy creates a new object, but does not create new objects for nested elements. Deep copy creates a new object and recursively copies all nested objects. For this purpose thecopy Module.

Question: How to merge two DataFrames in Pandas?

Answer: Using the Pandasmerge function to merge two DataFrames based on a common column.

Example:

merged_df = (df1, df2, on='common_column')

Question: Explain the purpose of virtual environments in Python.

Answer: Virtual environments are used to create isolated Python environments for different projects. Virtual environments allow you to manage dependencies and avoid conflicts between project-specific packages.

Question: How to deal with unbalanced datasets in machine learning?

Answer: Techniques for dealing with unbalanced datasets include resampling methods (over-sampling a few classes or under-sampling the majority), using different evaluation metrics, and employing algorithms that deal well with class imbalances.

Question: What does the "requests" library in Python do?

Answer: The "requests" library is used to make HTTP requests in Python. It simplifies the process of sending HTTP requests and processing responses.

Question: How to write unit tests in Python?

Answer: Pythonunittest module provides a framework for writing and running unit tests. Test cases are subclassed and created using various assertion methods to check for expected results.

Question: Explain the difference between iloc and loc in Pandas.

Answer: ilocis used for integer position-based indexing, whilelocis a tag-based index.ilocis primarily driven by integers, and thelocThen use labels to reference rows or columns.

Question: What does the pickle module in Python do?

Answer: picklemodule is used to serialize and deserialize Python objects. It allows objects to be saved to a file and then loaded, preserving their structure and state.

Question: How to execute code in parallel in Python?

Answer: Python provides the modules. the "ThreadPoolExecutor" and "ProcessPoolExecutor" classes can be used to execute tasks in parallel using threads or processes.

Question: Write a Python function to remove missing values from a pandas DataFrame.

Answers:

def remove_missing_values(df)：
    (inplace=True)
    come (or go) back df

Question: Write a Python function to recognize and handle outliers in a NumPy array.

Answers:

def handle_outliers(array)：
    # Use z-scores to identify outliers
    z_scores = (array - (array)) / (array)
    outliers = array[z_scores > 3].
    # Replacement of outliers with medians or means
    outlier_indices = (z_scores > 3)[0] # Replacement of outliers with medians or means
    array[outlier_indices] = (array)
    Returns an array

Question: Write a Python script to clean and prepare a CSV dataset for analysis.

Answers:

import pandas as pd
# Read the CSV file into a pandas DataFrame
data = pd.read_csv('')
# Handle missing values
(inplace=True)
# Handle outliers
for column in :
    data[column] = handle_outliers(data[column])
# Encode categorical variables
for column in :
    if data[column].dtypes == 'object':
        data[column] = data[column].astype('category').
# Save the cleaned DataFrame
data.to_csv('cleaned_data.csv', index=False)

Question: Write a Python function to calculate the mean, median, mode, and standard deviation of a data set.

Answers:

import pandas as pd
def calculate_descriptive_stats(data):
    stats_dict = {}
    # Calculate mean
    stats_dict['mean'] = ()
    # Calculate median
    stats_dict['median'] = ()
    # Calculate mode
    if  == 'object':
        stats_dict['mode'] = ()[0]
    else:
        stats_dict['mode'] = (data)
    # Calculate standard deviation
    stats_dict['std_dev'] = ()
    return stats_dict

Question: Write a Python script to perform linear regression using scikit-learn.

Answers:

from sklearn.linear_model import LinearRegression
# Load the data
X = ...  # Input features
y = ...  # Target variable
# Create and fit the linear regression model
model = LinearRegression()
(X, y)
# Make predictions
predictions = (X)

Question: Write a Python function that evaluates the performance of a classification model using accuracy, precision, and recall.

Answers:

from  import accuracy_score, precision_score, recall_score
def evaluate_classification_model(y_true, y_pred):
    accuracy = accuracy_score(y_true, y_pred)
    precision = precision_score(y_true, y_pred)
    recall = recall_score(y_true, y_pred)
    return {'accuracy': accuracy, 'precision': precision, 'recall': recall}

Question: Write Python scripts using Matplotlib or Seaborn to create data visualizations.

Answers:

import  as plt
# Generate data
data = ...
# Create a bar chart
(data['categories'], data['values'])
('Categories')
('Values')
('Data Visualization')
()

Question: Write Python scripts that communicate data-driven insights to non-technical stakeholders using clear and concise language.

Answers:

# Analyze the data and identify key insights
insights = ...
# Prepare a presentation or report using clear and concise language
presentation = ...
# Communicate insights to stakeholders using visuals and storytelling
present_insights(presentation)

Question: Write a Python function to remove missing values from a pandas DataFrame.

Answers:

def remove_missing_values(df):
    (inplace=True)
    return df

Question: Write a Python function to recognize and handle outliers in a NumPy array.

Answers:

def handle_outliers(array):
    # Identify outliers using z-score
    z_scores = (array - (array)) / (array)
    outliers = array[z_scores > 3]
    # Replace outliers with median or mean
    outlier_indices = (z_scores > 3)[0]
    array[outlier_indices] = (array)
    return array

concern： Write a Python function that evaluates the performance of a classification model using accuracy, precision, and recall.

Answers:

from  import accuracy_score, precision_score, recall_score
def evaluate_classification_model(y_true, y_pred):
    accuracy = accuracy_score(y_true, y_pred)
    precision = precision_score(y_true, y_pred)
    recall = recall_score(y_true, y_pred)
    return {'accuracy': accuracy, 'precision': precision, 'recall': recall}

Question: Write a Python function that splits a dataset into a training set and a test set.

Answers:

# Split the dataset into training and testing sets
from sklearn.model_selection import train_test_split
def split_dataset(data, test_size=0.2):
    # Separate features (X) and target variable (y)
    X = ('target_variable', axis=1)
    y = data['target_variable']
    # Split the dataset
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
    return X_train, X_test, y_train, y_test

Question: Write a Python script to perform k-means clustering using scikit-learn.

Answers:

# Perform k-means clustering
from  import KMeans
# Load the data
data = ...
# Create and fit the k-means model with a specified number of clusters (., 4)
model = KMeans(n_clusters=4)
(data)
# Predict cluster labels for each data point
cluster_labels = (data)

Question: Write a Python function to find the correlation between two variables.

Answers:

# Calculate the correlation between two variables
from  import pearsonr
def calculate_correlation(x, y):
    correlation = pearsonr(x, y)
    return correlation[0]

Question: Write a Python script to perform principal component analysis (PCA) using scikit-learn.

Answers:

# Perform principal component analysis (PCA)
from  import PCA
# Load the data
data = ...
# Create and fit the PCA model with a specified number of components (., 2)
model = PCA(n_components=2)
transformed_data = model.fit_transform(data)

Question: Write a Python function that normalizes a data set.

Answers:

# Normalize the dataset
from  import StandardScaler
def normalize_dataset(data):
    # Use StandardScaler to normalize the data
    scaler = StandardScaler()
    normalized_data = scaler.fit_transform(data)
    return normalized_data

Question: Write a Python script to perform dimensionality reduction using t-SNE.

Answers:

from  import TSNE
# Load the data
data = ...
# Create and fit the t-SNE model
model = TSNE(n_components=2)
reduced_data = model.fit_transform(data)

Question: Write a Python function that implements a custom loss function for a machine learning model.

Answers:

import tensorflow as tf

def custom_loss_function(y_true, y_pred):
    loss = .categorical_crossentropy(y_true, y_pred)
    return loss

Question: Write Python scripts using TensorFlow to train custom neural network models.

Answers:

import tensorflow as tf
# Define the model architecture
model = ([
    (64, activation='relu', input_shape=([1],)),
    (32, activation='relu'),
    (10, activation='softmax')
])
# Compile the model
(loss='custom_loss_function', optimizer='adam', metrics=['accuracy'])
# Train the model
(X_train, y_train, epochs=10, batch_size=32)

Source: /44-python-data-analyst-interview-questions/ 

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