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Python is the backbone of modern data analysis, and mastering libraries like NumPy and Pandas is essential for any data scientist. This cheat sheet covers everything from array operations to advanced DataFrame manipulations.
NumPy Essentials
import numpy as np Create an array arr = np.array([1, 2, 3]) Reshape array arr_2d = arr.reshape(1, 3) Matrix operations matrix = np.random.rand(3, 3) inverse = np.linalg.inv(matrix) Statistical functions mean = np.mean(arr) std_dev = np.std(arr)
Pandas for Data Manipulation
import pandas as pd
Create DataFrame
df = pd.DataFrame({'A': [1, 2, None], 'B': ['x', 'y', 'z']})
Handle missing data
df_cleaned = df.dropna()
Group and aggregate
grouped = df.groupby('B').sum()
Merge DataFrames
df2 = pd.DataFrame({'B': ['x', 'y'], 'C': [10, 20]})
merged = pd.merge(df, df2, on='B')
Advanced Data Analysis
Rolling window calculations df['rolling_avg'] = df['A'].rolling(window=2).mean() Expanding sum df['expanding_sum'] = df['A'].expanding().sum() Pivot tables pivot = df.pivot_table(values='A', index='B', aggfunc='mean')
You Should Know:
- NumPy is optimized for numerical computations, making it faster than native Python lists.
- Pandas excels in structured data operations, similar to SQL but more flexible.
- Always use `df.isnull().sum()` to check missing values before analysis.
- For large datasets, consider Dask or Vaex as Pandas alternatives.
Linux Command for Data Processing
Convert CSV to JSON using jq
cat data.csv | csvtojson | jq '.' > output.json
Process large logs with awk
awk '{print $1, $5}' access.log > filtered_logs.txt
Parallel processing with GNU Parallel
parallel -j 4 python process_data.py ::: data_.csv
Windows PowerShell for Data Handling
Import CSV
$data = Import-Csv "data.csv"
Filter and export
$data | Where-Object { $_.Value -gt 100 } | Export-Csv "filtered_data.csv"
Bulk rename files
Get-ChildItem .csv | Rename-Item -NewName { $<em>.Name -replace "old</em>", "new_" }
What Undercode Say:
Python remains the king of data analysis, but efficiency comes with practice. Automate repetitive tasks with Bash scripting or PowerShell. For big data, explore PySpark or CuDF (GPU-accelerated Pandas).
Prediction:
The future of data analysis will integrate more AI-driven automation, reducing manual preprocessing. Tools like Polars (Rust-based DataFrame) will gain traction for speed.
Expected Output:
A structured, efficient workflow combining Python, command-line tools, and automation for seamless data analysis.
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References:
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Extra Hub: Undercode MoN
Basic Verification: Pass ✅
