<\/span>Data Analyst Python Interview Questions \u2013 Intermediate Level<\/span><\/h2>\n\n\n\nLet\u2019s take a look at some intermediate level Python interview questions and answers for data analyst.<\/p>\n\n\n\n
\n- How do you handle missing data in a dataset using Pandas? Provide examples.<\/strong><\/li>\n<\/ol>\n\n\n\n
To handle missing data, you can use:<\/p>\n\n\n\n
\n- dropna():<\/strong> Removes rows or columns with missing values.<\/li>\n<\/ul>\n\n\n\n
df = df.dropna() <\/p>\n\n\n\n
\n- fillna(): <\/strong>Replaces missing values with a specified value.<\/li>\n<\/ul>\n\n\n\n
df[‘column’].fillna(df[‘column’].mean(), inplace=True) <\/p>\n\n\n\n
\n- Interpolation: <\/strong>Fills missing values based on patterns.<\/li>\n<\/ul>\n\n\n\n
df[‘column’] = df[‘column’].interpolate() <\/p>\n\n\n\n
\n- Explain the difference between .loc[] and .iloc[] in Pandas.<\/strong><\/li>\n<\/ol>\n\n\n\n
\n- .loc[]:<\/strong> Accesses rows or columns by labels (names).<\/li>\n\n\n\n
- .iloc[]:<\/strong> Accesses rows or columns by index positions.<\/li>\n<\/ul>\n\n\n\n
Example:<\/p>\n\n\n\n
# Using .loc[]<\/p>\n\n\n\n
print(df.loc[0:2, ‘column_name’]) <\/p>\n\n\n\n
# Using .iloc[]<\/p>\n\n\n\n
print(df.iloc[0:2, 1]) <\/p>\n\n\n\n
\n- How can you merge two datasets in Python? Explain with an example.<\/strong><\/li>\n<\/ol>\n\n\n\n
This is one of the most important data analyst Python interview questions.<\/p>\n\n\n\n
You can use the merge() function to combine datasets. Example:<\/p>\n\n\n\n
import pandas as pd <\/p>\n\n\n\n
df1 = pd.DataFrame({‘ID’: [1, 2], ‘Name’: [‘Alice’, ‘Bob’]}) <\/p>\n\n\n\n
df2 = pd.DataFrame({‘ID’: [1, 2], ‘Age’: [25, 30]}) <\/p>\n\n\n\n
merged = pd.merge(df1, df2, on=’ID’) <\/p>\n\n\n\n
print(merged) <\/p>\n\n\n\n
This merges the datasets based on the common ID column.<\/p>\n\n\n\n
\n- What are lambda functions in Python? How are they useful in data analysis?<\/strong><\/li>\n<\/ol>\n\n\n\n
Lambda functions are anonymous functions defined using the lambda keyword. They are useful for short, one-line operations, such as applying transformations to a dataset.<\/p>\n\n\n\n
Example:<\/p>\n\n\n\n
df[‘new_column’] = df[‘column’].apply(lambda x: x * 2) <\/p>\n\n\n\n