Complete Guide to Anaconda, Conda, and Jupyter for Beginners

Your Machine
├── base environment (Python 3.11)
├── project_a_env (Python 3.8, pandas 1.3)
└── project_b_env (Python 3.11, pandas 2.1)

conda --version
# Output: conda 24.x.x

python --version
# Output: Python 3.11.x

# Download installer (macOS example)
wget https://repo.anaconda.com/archive/Anaconda3-2024.10-MacOSX-x86_64.sh

# Run installer
bash Anaconda3-2024.10-MacOSX-x86_64.sh

# Follow prompts, then initialize conda
conda init

# Restart terminal, then verify
conda --version

# Linux/macOS
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

# Windows — download .exe from:
# https://docs.conda.io/en/latest/miniconda.html

# Check conda version
conda --version

# Detailed conda info (Python version, platform, channels)
conda info

# Help for any command
conda --help
conda create --help

# Update conda itself
conda update conda

# Create a new environment
conda create --name myenv

# Create with specific Python version
conda create --name myenv python=3.10

# Create with specific Python + packages
conda create --name myenv python=3.10 numpy pandas matplotlib

# List all environments
conda env list
# OR
conda info --envs

# Activate an environment
conda activate myenv

# Deactivate current environment
conda deactivate

# Remove an environment completely
conda env remove --name myenv

# Clone an environment (copy)
conda create --name myenv_backup --clone myenv

# Rename environment (conda doesn't rename directly)
# Step 1: Clone
conda create --name new_name --clone old_name
# Step 2: Delete old
conda env remove --name old_name

# Install a package (in active environment)
conda install numpy

# Install specific version
conda install numpy=1.24.3

# Install multiple packages at once
conda install numpy pandas matplotlib scikit-learn

# Install from conda-forge channel
conda install -c conda-forge plotly

# Update a package
conda update numpy

# Update all packages in environment
conda update --all

# Remove a package
conda remove numpy

# List all installed packages
conda list

# Search for a package
conda search pandas

# Check if package exists in conda-forge
conda search -c conda-forge tensorflow

# Install using pip inside conda environment
pip install some_package  # only if not available via conda

# Export current environment to a YAML file
conda env export > environment.yml

# Export only packages you explicitly installed (cleaner)
conda env export --from-history > environment.yml

# Example environment.yml output:

name: myenv
channels:
  - conda-forge
  - defaults
dependencies:
  - python=3.10
  - numpy=1.24.3
  - pandas=2.0.1
  - matplotlib=3.7.1
  - scikit-learn=1.3.0
  - pip:
    - some_pip_only_package==1.0.0

# Create environment FROM a YAML file
conda env create -f environment.yml

# Update existing environment from YAML
conda env update -f environment.yml --prune

# Default channels: defaults (Anaconda's official repo)
# conda-forge: community channel with many more packages

# Add conda-forge as default channel
conda config --add channels conda-forge
conda config --set channel_priority strict

# Install from specific channel
conda install -c conda-forge lightgbm

# Show configured channels
conda config --show channels

# Remove a channel
conda config --remove channels conda-forge

conda-forge > defaults  (recommended for data science)

# Show all configurations
conda config --show

# Set auto-activation of base environment OFF (good practice)
conda config --set auto_activate_base false

# Always use conda-forge
conda config --add channels conda-forge
conda config --set channel_priority strict

# Show config file location
conda config --show-sources

# Remove unused packages and caches
conda clean --all

# Remove only cached packages (tarballs)
conda clean --tarballs

# Remove unused packages
conda clean --packages

# Dry run (see what will be deleted)
conda clean --all --dry-run

# Conda install
conda install numpy

# Pip install (use only when conda doesn't have the package)
pip install numpy

# Check what's installed
conda list          # shows conda + pip packages
pip list            # shows only pip packages

# Uninstall
conda remove numpy
pip uninstall numpy

# Activate your environment first
conda activate myenv

# Install jupyter if not present
conda install jupyter

# Launch Jupyter Notebook
jupyter notebook

# Launch on a specific port
jupyter notebook --port 8889

# Launch without opening browser
jupyter notebook --no-browser

# Launch on a specific directory
jupyter notebook --notebook-dir="C:/Projects/my_project"

Browser: http://localhost:8888
│
├── Dashboard (File Browser)
│   ├── New → Python 3 (creates new notebook)
│   ├── Upload (upload .ipynb or data files)
│   └── Running (see active notebooks)
│
└── Notebook Editor
    ├── Menu Bar (File, Edit, View, Cell, Kernel, Help)
    ├── Toolbar (Run, Stop, Restart, Cell type selector)
    └── Cells (Code cells, Markdown cells, Raw cells)

Change cell type:
- Via dropdown in toolbar
- Keyboard: Press Esc → M (Markdown), Y (Code), R (Raw)

COMMAND MODE (press Esc to enter):
─────────────────────────────────
A          → Insert cell Above
B          → Insert cell Below
D, D       → Delete current cell
M          → Convert to Markdown
Y          → Convert to Code
Z          → Undo cell deletion
Shift + M  → Merge selected cells
Up/Down    → Navigate cells
Shift + Enter → Run cell and move to next
Ctrl + Enter  → Run cell and stay

EDIT MODE (press Enter to enter):
──────────────────────────────────
Tab        → Autocomplete
Shift+Tab  → Show docstring/help
Ctrl + /   → Comment/uncomment code
Ctrl + Z   → Undo
Ctrl + A   → Select all
Ctrl + D   → Delete line

# Cell 1: Basic Python
x = 10
y = 20
print(x + y)
# Output: 30

# Cell 2: Variables persist across cells in same notebook
z = x * y   # x and y from Cell 1 are available
print(z)
# Output: 200

# Cell 3: Last expression is auto-displayed
x + y       # No need to print — Jupyter shows it automatically
# Output: 30

# Cell 4: Multiple outputs
x = 5
y = 10
x, y        # Shows (5, 10) — last expression displayed

# Time a single expression
%time sum(range(1_000_000))
# Output: CPU times: user 12.1 ms, sys: 0 ns, total: 12.1 ms

# Time multiple runs and give average
%timeit sum(range(1_000_000))
# Output: 10.1 ms ± 234 µs per loop (mean ± std. dev. of 7 runs, 100 loops)

# List all variables in namespace
%who
%whos      # more detailed version

# Load a Python file into cell
%load my_script.py

# Run a Python file
%run my_script.py

# Show current working directory
%pwd

# Change directory
%cd /path/to/folder

# List files in directory
%ls

# Show command history
%history

# Reset all variables (clear namespace)
%reset

# Install packages without leaving notebook
%pip install seaborn
%conda install seaborn

# Show matplotlib plots inline
%matplotlib inline

# Interactive plots (hover, zoom)
%matplotlib widget

%%time
# Time entire cell execution
import pandas as pd
df = pd.DataFrame({'a': range(100000)})
df['b'] = df['a'] * 2

%%writefile my_script.py
# Write cell content to a file
def greet(name):
    return f"Hello, {name}!"

print(greet("Himanshu"))

%%html
<!-- Render HTML inside notebook -->
<h1 style="color:blue">Hello from HTML!</h1>
<button>Click Me</button>

%%bash
# Run bash/shell commands in cell
echo "Current directory:"
pwd
ls -la

%%sql
-- If you have ipython-sql installed
SELECT * FROM employees LIMIT 10;

%%capture output
# Capture cell output (don't display, store in variable)
import pandas as pd
df = pd.read_csv('data.csv')
print(df.shape)
# Access via: output.stdout

# Cell 1: Import libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline   # Show plots inside notebook

print("Libraries loaded!")

# Cell 2: Create sample dataset
np.random.seed(42)
df = pd.DataFrame({
    'Month': pd.date_range('2023-01', periods=12, freq='ME'),
    'Sales': np.random.randint(50000, 200000, 12),
    'Expenses': np.random.randint(30000, 150000, 12),
    'Region': np.random.choice(['North', 'South', 'East', 'West'], 12)
})

df['Profit'] = df['Sales'] - df['Expenses']
df.head()   # Displays as formatted HTML table in Jupyter

# Cell 3: Quick stats
df.describe()

# Cell 4: Plot
fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Line chart
axes[0].plot(df['Month'], df['Sales'], marker='o', color='steelblue', label='Sales')
axes[0].plot(df['Month'], df['Expenses'], marker='s', color='salmon', label='Expenses')
axes[0].set_title('Sales vs Expenses (Monthly)')
axes[0].legend()
axes[0].tick_params(axis='x', rotation=45)

# Bar chart
axes[1].bar(df['Month'].dt.strftime('%b'), df['Profit'], color='green')
axes[1].set_title('Monthly Profit')
axes[1].tick_params(axis='x', rotation=45)

plt.tight_layout()
plt.show()

# Main Heading
## Sub Heading
### Sub-Sub Heading

**Bold text**
*Italic text*
`inline code`

- Bullet item 1
- Bullet item 2

1. Numbered item 1
2. Numbered item 2

| Column A | Column B |
|----------|----------|
| Value 1  | Value 2  |

$$E = mc^2$$          ← LaTeX math equations

[Link text](https://example.com)

![Image](image.png)

> Blockquote

# Convert notebook to Python script
jupyter nbconvert --to script notebook.ipynb
# Creates: notebook.py

# Convert to HTML (share with non-Python users)
jupyter nbconvert --to html notebook.ipynb
# Creates: notebook.html

# Convert to PDF (requires LaTeX)
jupyter nbconvert --to pdf notebook.ipynb

# Convert to Markdown
jupyter nbconvert --to markdown notebook.ipynb

# Execute notebook and save output
jupyter nbconvert --to notebook --execute notebook.ipynb --output executed_notebook.ipynb

# Run notebook as a script (headless)
jupyter nbconvert --execute notebook.ipynb

# Install ipykernel in your environment
conda activate myenv
conda install ipykernel

# Register environment as a Jupyter kernel
python -m ipykernel install --user --name myenv --display-name "Python (myenv)"

# List all available kernels
jupyter kernelspec list

# Remove a kernel
jupyter kernelspec remove myenv

# Check which kernel a notebook is using
# → Kernel menu → Change Kernel

# Install JupyterLab
conda install jupyterlab

# Launch
jupyter lab
# Opens at: http://localhost:8888/lab

# Install useful extensions
pip install jupyterlab-git          # Git integration
pip install jupyterlab-code-formatter  # Auto-format code
pip install jupyterlab_execute_time  # Show cell execution time
pip install aquirdturtle_collapsible_headings  # Collapsible sections

# List installed extensions
jupyter labextension list

# Export
conda env export --from-history > environment.yml

# Teammate creates environment
conda env create -f environment.yml

conda activate myenv
conda install ipykernel
python -m ipykernel install --user --name myenv
# Then in Jupyter → Kernel → Change Kernel → Select myenv

jupyter nbconvert --to notebook --execute input.ipynb --output output.ipynb

# 1. Parameterize notebook with papermill
# 2. Run via cron/Task Scheduler:
papermill template.ipynb output_$(date +%Y%m%d).ipynb -p date $(date +%Y-%m-%d)

# 3. Convert to HTML
jupyter nbconvert output_*.ipynb --to html

# 4. Email via Python smtplib or SendGrid

conda install -n base conda-libmamba-solver
conda config --set solver libmamba
# Now conda uses the libmamba C++ solver → 10-50x faster