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Python Code Library — Ready-to-Run Recipes

5 ready-to-run snippet(s) · curated with sample data, dataset sources & use cases

Data Analysis🎓 Beginner

Pandas EDA — Profile any CSVLoads any CSV and prints shape, dtypes, missing-value counts, descriptive stats, and a correlation heatmap.

#pandas#eda#seaborn

Data AnalysisBeginner

Pandas EDA — Profile any CSV

Loads any CSV and prints shape, dtypes, missing-value counts, descriptive stats, and a correlation heatmap.

🎯 Use Case

First-pass exploratory data analysis for survey, health, or socio-economic data.

👥 Audience

Beginners learning pandas; analysts profiling unfamiliar datasets.

Data to use with this code

Kaggle — World Happiness Report ↗

sample.csv · csv

country,year,gdp,life_expectancy,happiness
Bangladesh,2023,2500,72.6,4.28
India,2023,2600,70.1,4.04
Nepal,2023,1300,70.8,5.39
Bhutan,2023,3500,71.8,5.17

py-pandas-eda.py

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

df = pd.read_csv('sample.csv')
print('Shape:', df.shape)
print('\nDtypes:\n', df.dtypes)
print('\nMissing:\n', df.isna().sum())
print('\nDescribe:\n', df.describe())

num = df.select_dtypes('number')
sns.heatmap(num.corr(), annot=True, cmap='RdBu_r', center=0)
plt.title('Correlation Matrix')
plt.tight_layout(); plt.show()

📤 Expected Outputs

Shape & dtypes printout
Missing-value counts
Correlation heatmap

#pandas#eda#seaborn

Geospatial🎓 Intermediate

GeoPandas Choropleth MapJoin a CSV indicator to a shapefile/GeoJSON of districts and render a classified choropleth.

#geopandas#choropleth#mapping

GeospatialIntermediate

GeoPandas Choropleth Map

Join a CSV indicator to a shapefile/GeoJSON of districts and render a classified choropleth.

🎯 Use Case

Visualize population density, poverty rate, NDVI averages, or disease prevalence by admin unit.

👥 Audience

GIS analysts, public health researchers, students working with admin-level data.

Data to use with this code

GADM — Country boundaries ↗

indicator.csv · csv

district,literacy_rate
Dhaka,76.5
Chattogram,73.2
Khulna,74.1
Rajshahi,71.8
Sylhet,68.9
Barishal,72.5

Pair with a GADM Level-1 GeoJSON of Bangladesh.

py-geopandas-choropleth.py

import geopandas as gpd, pandas as pd, matplotlib.pyplot as plt

gdf = gpd.read_file('bangladesh_adm1.geojson')
df = pd.read_csv('indicator.csv')
merged = gdf.merge(df, left_on='NAME_1', right_on='district')

fig, ax = plt.subplots(figsize=(8, 9))
merged.plot(column='literacy_rate', cmap='YlGnBu', legend=True,
            edgecolor='white', linewidth=0.6, ax=ax,
            scheme='Quantiles', k=5)
ax.set_title('Literacy Rate by District'); ax.axis('off')
plt.tight_layout(); plt.show()

📤 Expected Outputs

Classified choropleth (quantiles)

#geopandas#choropleth#mapping

Machine Learning🎓 Intermediate

scikit-learn Random Forest ClassifierTrain/test split, RF model, accuracy + confusion matrix + feature importance.

#scikit-learn#random-forest#classification

Machine LearningIntermediate

scikit-learn Random Forest Classifier

Train/test split, RF model, accuracy + confusion matrix + feature importance.

🎯 Use Case

Predict outcomes from tabular survey, health, or remote-sensing features.

👥 Audience

ML beginners with basic Python knowledge.

Data to use with this code

UCI ML Repository ↗

py-ml-rf.py

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, ConfusionMatrixDisplay
import pandas as pd, matplotlib.pyplot as plt

df = pd.read_csv('data.csv')
X = df.drop(columns=['target']); y = df['target']
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
clf = RandomForestClassifier(n_estimators=200, random_state=42).fit(Xtr, ytr)
print(classification_report(yte, clf.predict(Xte)))
ConfusionMatrixDisplay.from_estimator(clf, Xte, yte); plt.show()
pd.Series(clf.feature_importances_, index=X.columns).sort_values().tail(15).plot.barh()
plt.title('Feature Importance'); plt.tight_layout(); plt.show()

📤 Expected Outputs

Classification report
Confusion matrix
Top-15 feature importance

#scikit-learn#random-forest#classification

Geospatial🎓 Beginner

Rasterio NDVI from Local TIFFRead Red and NIR bands from a multiband GeoTIFF, compute NDVI, and save the result.

#rasterio#ndvi#numpy

GeospatialBeginner

Rasterio NDVI from Local TIFF

Read Red and NIR bands from a multiband GeoTIFF, compute NDVI, and save the result.

🎯 Use Case

Offline NDVI workflow when you already downloaded scenes from USGS or Copernicus.

👥 Audience

Remote sensing students working locally without GEE.

Data to use with this code

USGS EarthExplorer ↗

py-rasterio-ndvi.py

import rasterio, numpy as np
with rasterio.open('scene.tif') as src:
    red = src.read(4).astype('float32')
    nir = src.read(5).astype('float32')
    profile = src.profile
ndvi = (nir - red) / (nir + red + 1e-9)
profile.update(count=1, dtype='float32')
with rasterio.open('ndvi.tif','w', **profile) as dst:
    dst.write(ndvi, 1)
print('Saved ndvi.tif — mean:', float(np.nanmean(ndvi)))

📤 Expected Outputs

ndvi.tif single-band raster
Mean NDVI printout

#rasterio#ndvi#numpy

Public Health🎓 Advanced

DHS Survey Weighted MeansCompute weighted means and 95% CIs from a DHS Stata file using the survey design.

#dhs#survey#samplics

Public HealthAdvanced

DHS Survey Weighted Means

Compute weighted means and 95% CIs from a DHS Stata file using the survey design.

🎯 Use Case

Maternal & child health indicators from Demographic & Health Surveys.

👥 Audience

Public health researchers, epidemiologists.

Data to use with this code

DHS Program datasets ↗

py-dhs-survey.py

import pandas as pd
from samplics.estimation import TaylorEstimator
df = pd.read_stata('BDIR81FL.DTA', convert_categoricals=False)
df['wt'] = df['v005'] / 1_000_000
est = TaylorEstimator('mean').estimate(
    y=df['v201'], samp_weight=df['wt'], stratum=df['v023'], psu=df['v021']
)
print(est.to_dataframe())

📤 Expected Outputs

Mean, SE, 95% CI table

#dhs#survey#samplics