50 Essential Pandas Interview Questions and Answers [2026]

Pandas is an open-source Python library that offers powerful, built-in methods for efficiently cleaning, analyzing, and manipulating datasets. Wes McKinney developed this package in 2008, and it integrates easily with many other data science modules in Python. It is important to know that Pandas is actually built on top of the NumPy library, which means its primary data structures like Series and DataFrame are essentially labeled, enhanced versions of NumPy arrays. This underlying structure is why Pandas operations are so fast and efficient for handling tabular data tasks.

Pandas offers two main structures that we rely on for data manipulation. These are the Series, which handles one-dimensional data, and the DataFrame, which handles two-dimensional data.

A Series is a one-dimensional labeled array designed to hold homogeneous data, meaning all its values must be of the same data type. We can think of a Series as similar to a single column in a table. A DataFrame is a two-dimensional tabular structure composed of multiple rows and columns. A major strength of the DataFrame is that each of its columns can hold different data types, meaning it supports heterogeneous data. We use the Series when we need speed and less memory consumption for specific tasks, but DataFrames are necessary for handling complex and large datasets because they allow for a much wider range of operations.

We have several ways to create a DataFrame. Two common methods involve using a Python dictionary, where the keys naturally become the column names, or using a list of lists along with an explicit list of column names. For example, a list of dictionaries where each dictionary represents a row is also a very common creation method.

To get a quick preview of our data, we use the head() method to access the first five rows of a DataFrame. We use the tail() method to access the last five rows. We can customize how many rows we want to see by simply passing an integer argument into either method, such as df.head(10) for the top ten rows.

In Pandas, shape is considered an attribute of the DataFrame object, not a method. Attributes are inherent properties, such as the total count of rows and columns, that do not require any input arguments or calculation to return the result. Because it is an attribute, we access it directly without the function-calling parentheses.

The index is a crucial feature in Pandas, representing a series of labels that uniquely identify each row of a DataFrame. The index can use various data types, such as integers, strings, or even timestamps. Its importance is rooted in providing flexible indexing and dynamic data alignment, which allows for fast, label-based data retrieval and efficient operations during critical tasks like merging and joining multiple datasets.

We use the powerful input/output function pd.read_csv(). We provide the file path or URL for the CSV file as the required argument to this function, and it efficiently loads the data directly into a Pandas DataFrame structure.

We use simple bracket notation, much like accessing items in a dictionary, by passing the column name as a string, for example, df[‘Column Name’]. This operation always returns the data for that column as a Pandas Series.

To select multiple columns, we must pass a list of column names inside the bracket notation, like df]. Since we are selecting more than one column, the result of this operation is a new DataFrame, not a Series.

We use the describe() method. This method quickly calculates descriptive statistics for the numerical columns, providing key metrics such as the count of non-null values, the mean, the standard deviation, the minimum and maximum values, and the quartile ranges.

We apply the function Series.value_counts() to the specific column of interest. This method efficiently returns a Series showing the frequency count of each unique value present within that categorical column.

The info() method provides a quick, concise summary of the DataFrame. This is extremely useful for initial data quality checks, as it shows us the column names, the data type of each column, the count of non-null values per column, and the overall memory usage of the DataFrame. This allows us to quickly assess data completeness and determine if memory optimization is necessary.

Pandas is widely valued because of its robust data structures, particularly the DataFrame. We appreciate its rich toolsets dedicated to data cleaning, transformation, and merging. It offers extensive functionality for working with time-series data, and its easy input and output capabilities allow us to interact effortlessly with various data sources, including CSVs, Excel files, and SQL databases.

NumPy focuses primarily on fast, homogeneous, unlabeled, numerical array computation. Pandas, while built upon NumPy, adds labels for rows and columns and specializes in high-level data manipulation and analysis of messy, real-world data. The key difference is that NumPy arrays must have all elements of the same type, while DataFrames can accommodate mixed data types across different columns, offering much more flexibility for real data science work.

Both loc and iloc are used to select subsets of data, but they operate differently. We must use the loc method when we want to select data based on the explicit labels of rows and columns, and its slicing is inclusive of the endpoint. Conversely, we use the iloc method when we need to extract data based on the integer positions of rows and columns, starting at zero, and its slicing is exclusive of the endpoint, similar to standard Python slicing behavior.

The at and iat functions are designed for high-speed access to a single scalar value. The at function accesses a value using explicit labels, and the iat function accesses a value using integer positions. They offer faster performance than their counterparts because they skip the overhead involved in general selection processes, making them the preferred choice when repeatedly accessing or setting a single cell, such as inside an iterative loop.

MultiIndex, also known as hierarchical indexing, enables us to work with multiple levels of indexing along the row or column axis. This structure is necessary when we do not have a single column that can uniquely identify each row. For example, if we need a combination of “name” and “address” to ensure uniqueness, we can set both columns as the index, creating a MultiIndex. This complex structure is vital for handling hierarchical data, such as sales grouped by region and product.

Reindexing, performed using DataFrame.reindex(), allows us to create a new DataFrame object from an existing one but with an updated set of row indexes or column labels. This process is extremely useful when we need to conform one DataFrame to match the index structure of another. If the values for the new indexes were not present in the original DataFrame, Pandas automatically fills those positions with default null values (NaN).

This is a straightforward two-step process. First, we use the pd.read_excel() function, passing in the Excel file path, to load the data into a DataFrame variable. Second, we apply the to_csv() method to that DataFrame, for example, excel_data.to_csv(“CSV_data.csv”, index = None, header=True). It is important to note that setting index = None prevents the DataFrame’s internal index from being written as an extra column in the output CSV.

We check for missing values using the combination of methods df.isnull().sum(), which gives us a count of nulls per column. We handle these values primarily in two ways: we can completely remove rows or columns containing nulls using the dropna() method. Or, more commonly, we fill the missing values with replacement values, such as zero, the column mean, or a specific constant, using the fillna() method.

We have highly effective methods for filtering data based on conditions. The first method is using boolean indexing, which involves passing a boolean Series (resulting from a condition) into the DataFrame selection brackets, such as df[(df.Name == “John”) | (df.Marks > 90)]. A second, more concise method is using the query() function, which allows us to write SQL-like conditions as strings, for example, df.query(‘Name == “John” or Marks > 90’).

We use the sort_values() method to sort the entire DataFrame. We must specify the column name or a list of column names using the by argument, such as df.sort_values(by=[“column_names”]). We can also control the sorting direction (ascending or descending) using the ascending parameter.

We frequently create new columns by applying operations on existing data. For simple math, we use vectorized operations directly, like df = df[‘Col1’] + df[‘Col2’]. For more complex, row-wise conditional logic, we utilize the .apply() method, typically with a lambda function. For example, we might use apply() to categorize house types based on conditional logic like df[“notes”] = df.apply(lambda house_type: “Missing house type” if house_type == “Not Specified” else “”).

The applymap() function is specifically designed to apply a function element-wise across the entire DataFrame. This means it transforms every single value within the DataFrame independently of all other rows and columns. This is useful when we need to apply formatting, type conversion, or specific value transformations to every single cell in the dataset.

Timedelta represents a precise duration, meaning the difference between two separate dates or times. This structure allows us to perform time arithmetic accurately and is typically measured in standard units like days, hours, minutes, and seconds.

One-hot encoding is a crucial step for preparing categorical data for machine learning models. We use the built-in pd.get_dummies() function. This function automatically converts categorical variables into a set of new binary indicator columns, where a value of 1 represents the presence of that category and 0 represents its absence.

We use the drop_duplicates() method. By default, this method removes duplicate rows, keeping only the first occurrence of the data. We have control over which duplicate to keep by using the keep parameter, setting it to ‘first’, ‘last’, or False if we want to drop all rows involved in a duplication.

We use the .astype() method on the specific column we wish to modify. We then pass the desired data type, such as ‘int32’, ‘float64’, or the memory-efficient ‘category’, as an argument to the method. This helps ensure that the data types are optimized for both performance and memory usage.

If an Excel workbook contains data across several sheets, we use the pd.read_excel() function and set the sheet_name parameter equal to None. When we do this, the function returns an ordered dictionary where the keys are the names of the sheets and the values are the corresponding DataFrames for the data found in those sheets.

The fillna() method is used to replace missing values (NaN) with static values or calculated statistics, such as the column mean or a predetermined constant. In contrast, the interpolate() method estimates missing values by analyzing neighboring data points, typically assuming a continuous or linear trend between the known values. Interpolation is often the superior choice for time-series or sequential data where the relationship between adjacent points is meaningful and we want to preserve that pattern.

The groupby() method adheres to the industry standard “Split-Apply-Combine” paradigm. The first stage is Split, where the data is divided into groups based on the unique values in one or more specified columns. The second stage is Apply, where a function, such as calculating the sum or the mean, is performed independently on each of the groups. The final stage is Combine, where the results from all the individual group operations are collected and merged into a single resulting Series or DataFrame.

After we define our groups using the groupby() function on the desired column, we chain a standard aggregation function directly onto the resulting GroupBy object. For example, to find the average marks for each name, we use the code df.groupby(‘Name’).mean(). This powerful process summarizes large amounts of data quickly based on common categories.

The merge() function is highly versatile, operating much like a SQL join, and it allows us to explicitly specify the column or columns we want to join the DataFrames on using the on parameter. It defaults to an inner join, keeping only common keys. The join() method is simpler and primarily defaults to combining DataFrames based on their common index labels. It defaults to a left join. We generally use merge() for clear, explicit column-based joining, and join() when we are certain the indices are already correctly aligned for slight speed advantages.

The concat() function is the main tool we use for combining multiple DataFrames either vertically along rows (axis=0) or horizontally along columns (axis=1). It is flexible and handles more than two DataFrames simultaneously. The append() method is a simpler, row-only method used to combine two DataFrames. The concat() method is now generally preferred because it is more versatile and allows us to use parameters like ignore_index to reset the resulting index.

To perform multiple aggregations on data that has been grouped, we use the .agg() method immediately following the groupby() operation. We can pass a list of function names (like ‘mean’, ‘sum’, ‘count’) to apply those functions to all selected columns. Alternatively, we can use a dictionary to specify different functions for different columns, allowing for precise control over the resulting aggregation structure.

To ensure that all keys from both the left and the right DataFrames are included in the final result, we use the pd.merge() function and explicitly set the how parameter to the value ‘outer’. This full outer merge results in a combined DataFrame where matching rows are aligned, and non-matching rows are included, with missing values filled using NaN.

The rolling mean, or moving average, is a statistical calculation that computes the mean of data over a fixed size window of observations. This window moves sequentially across the data points. We frequently use the rolling mean in time-series analysis to smooth out short-term fluctuations or noise in the data, which helps us reveal the underlying, longer-term trends.

We use the powerful pd.pivot_table() function, which is designed for summarizing and rearranging data. To use it effectively, we must specify four key elements: the index (what defines the rows), the columns (what defines the new columns), the values (the data we want to aggregate), and the aggregation function (aggfunc), which defaults to calculating the mean.

Since the logarithm function is undefined for zero or negative inputs, we must preprocess the data carefully before transformation. A common and robust technique is to use conditional replacement, such as combining NumPy’s np.where with Pandas, to replace all negative values with a very small positive constant. This shifting process ensures that the logarithmic transformation is mathematically valid for the entire column.

When used after a groupby() operation, the functions apply() and transform() serve different roles. The apply() method is the most flexible, as it can execute complex custom operations and is allowed to return a result that changes the size or shape of the DataFrame or Series. The transform() method, in contrast, is strictly required to return a Series that has the exact same index and length as the original input group. This rigidity makes transform() essential when the calculation, like a group mean, needs to be mapped back onto every original row of the group, preserving the DataFrame structure for feature engineering.

The SettingWithCopyWarning is generated by Pandas when we appear to be attempting to assign a value to a temporary copy, or slice, of a DataFrame, rather than to the original data structure. This can cause confusion because the changes we intend to make might not persist in the primary DataFrame. To fix this issue and ensure we modify the original DataFrame, the solution is to use explicit indexing, such as chaining the selection with the assignment in a single command using .loc. This tells Pandas clearly that we are intentionally modifying the true underlying data.

Optimizing performance is critical when dealing with large datasets. We should focus on three main strategies: First, load only the necessary data by using the usecols parameter in pd.read_csv(), and use chunksize to process data sequentially in manageable parts. Second, avoid slow Python loops and instead use vectorized operations, which are significantly faster because they execute operations across entire columns at once. Third, use the appropriate data types, such as casting large integers to smaller integer types like int32 or utilizing the highly efficient category data type.

Vectorization is the process of applying operations simultaneously across entire arrays or columns of data, avoiding the need to iterate through data points one by one using inefficient Python loops. It is centrally important because Pandas leverages the underlying NumPy library, which utilizes highly optimized C code for these vectorized operations. This allows us to process vast amounts of data dramatically faster than if we wrote equivalent row-wise loops in standard Python code.

The category data type is a powerful tool for memory optimization, particularly for columns containing strings that have many repeated values. When we convert such a column to the category type, Pandas replaces the full string objects in the column with efficient, small integer codes, storing the unique string mapping only once. For DataFrames with millions of rows, this mechanism significantly reduces the overall memory footprint compared to storing the full, repeated Python string object for every single row.

Out-of-memory issues are common when dealing with data that exceeds the system’s RAM. A practical solution involves using the chunksize parameter when reading files with pd.read_csv(). This allows us to read and process the file sequentially in smaller, memory-friendly batches. For even larger, distributed workloads, we must utilize external parallel processing frameworks like Dask or Ray, which offer Pandas-like APIs but execute tasks across multiple cores or a cluster.

To work with time series data, we must first convert the relevant column to datetime objects and then set this column as the DataFrame’s index. Once we have a Datetime Index, we use the .resample() method. Resampling is used to change the frequency at which the data is reported. For example, we might aggregate high-frequency minute data up to a monthly frequency by specifying the new frequency (e.g., ‘M’ for monthly) and an aggregation function, such as .sum() or .mean().

Both functions are used for reshaping data, but they differ in handling duplicate entries. The pivot() function is specifically used when the combination of the index and columns must result in unique entries, meaning it is purely a reshaping operation that does not support aggregation. The pivot_table() function is much more robust and is used when aggregation is necessary. It handles instances where multiple values fall into the same new cell by calculating a summary statistic, such as the mean or sum, making it a general-purpose function for data summarization.

By itself, the core Pandas library is single-threaded, meaning it uses only one CPU core for processing. To achieve true parallel computing and distribute DataFrames tasks across multiple cores or a cluster, we need to integrate specialized external libraries. Frameworks like Dask or Modin provide APIs that closely mimic Pandas syntax but efficiently manage and distribute the underlying data processing, resulting in significant speed improvements when working with large-scale data.

Irregular time series data lacks a consistent time interval between observations. The first step is confirming that the data uses a proper Datetime Index. We then use the .resample() function to force the data onto a standard, regular frequency, such as daily or hourly, which introduces explicit missing values (NaN) where the timestamps were absent. We then fill these new gaps using appropriate methods, such as forward filling (ffill) to carry the last known observation forward, or by using interpolation methods to estimate the missing value based on known neighbors.