Inside the Interview Room · Python Careers 2026

What Companies Are Actually Hiring For: Python Requirements for Data Analysts

SECTION 01Python by role — comparison

Python requirements vary significantly by role. Here's what each role actually needs:

SECTION 02Data Analyst Python — what you need

Data Analysts use Python for data cleaning, analysis, and visualization. Here's what companies expect:

• pandas — data manipulation, reading/writing files, filtering, aggregation
• numpy — basic array operations, numerical computations
• matplotlib / seaborn — creating charts and visualizations
• Jupyter notebooks — interactive analysis and documentation
• Basic Python — variables, loops, functions, list comprehensions

SECTION 03pandas — the most important library

pandas is the most important Python library for data analysts. Here's what you need to know:

• Reading data — pd.read_csv(), pd.read_excel(), pd.read_sql()
• Data exploration — df.head(), df.info(), df.describe()
• Filtering — df[df['column'] > value]
• Grouping and aggregation — df.groupby('column').agg({'value': 'sum'})
• Data cleaning — handling missing values, renaming columns, dropping duplicates
• Merging — pd.merge() for joining DataFrames

Sample interview question: "Load a CSV file, clean it, and calculate the average sales by region."

import pandas as pd

# Load data
df = pd.read_csv('sales_data.csv')

# Explore data
print(df.head())
print(df.info())

# Clean data
df = df.dropna(subset=['sales_amount'])
df['sales_amount'] = df['sales_amount'].astype(float)

# Group by region and aggregate
region_summary = df.groupby('region').agg({
    'sales_amount': ['sum', 'mean', 'count']
}).reset_index()
region_summary.columns = ['region', 'total_sales', 'avg_sales', 'num_orders']

print(region_summary)

# Filter and sort
top_regions = region_summary[region_summary['total_sales'] > 100000].sort_values('total_sales', ascending=False)
print(top_regions)

SECTION 04numpy basics — arrays

numpy is used for numerical computing. Data Analysts need basic numpy skills:

• Creating arrays — np.array(), np.arange(), np.zeros()
• Basic operations — addition, multiplication, mean, standard deviation
• Indexing and slicing — accessing elements
• Broadcasting — operations on arrays of different shapes

import numpy as np

# Create arrays
arr1 = np.array([1, 2, 3, 4, 5])
arr2 = np.arange(10, 20, 2)  # [10, 12, 14, 16, 18]
zeros = np.zeros(5)

# Basic operations
print("Mean:", np.mean(arr1))
print("Standard deviation:", np.std(arr1))
print("Sum:", np.sum(arr1))

# Element-wise operations
print("Addition:", arr1 + arr2)
print("Multiplication:", arr1 * 2)

# Indexing
print("First 3 elements:", arr1[:3])
print("Elements > 3:", arr1[arr1 > 3])

SECTION 05Visualization — matplotlib & seaborn

Data Analysts need to create visualizations to communicate insights. Here's what you need to know:

• matplotlib — line plots, bar charts, scatter plots, histograms
• seaborn — statistical plots, heatmaps, pair plots
• Plot customization — titles, labels, legends, colors
• Saving plots — plt.savefig()

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

# Load data
df = pd.read_csv('sales_data.csv')

# Bar chart - matplotlib
plt.figure(figsize=(10, 6))
plt.bar(df['region'], df['sales_amount'])
plt.title('Sales by Region')
plt.xlabel('Region')
plt.ylabel('Sales Amount')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('sales_bar_chart.png')
plt.show()

# Histogram - seaborn
plt.figure(figsize=(10, 6))
sns.histplot(df['sales_amount'], bins=20, kde=True)
plt.title('Sales Distribution')
plt.xlabel('Sales Amount')
plt.ylabel('Frequency')
plt.tight_layout()
plt.show()

# Scatter plot - seaborn
plt.figure(figsize=(10, 6))
sns.scatterplot(data=df, x='ad_spend', y='sales_amount', hue='region')
plt.title('Ad Spend vs Sales')
plt.tight_layout()
plt.show()

SECTION 06What interviewers test

Here's how Python is tested in data analyst interviews:

• pandas operations — filtering, grouping, merging, aggregating
• Data cleaning — handling missing values, converting data types
• Visualization code — creating charts with matplotlib/seaborn
• Problem-solving — write code to solve a data problem
• Explaining your code — why did you use this approach?

SECTION 07Python practice plan

Here's a 6-week plan to build the Python skills companies actually want:

SECTION 08Interview Q&A — Python for data analysts

SECTION 09Test yourself — Python readiness quiz

SECTION 10Frequently asked questions

SECTION 11Related reads