IndLan (v2.0)

IndLan is a modern programming language and Hindi + English interface for the entire Python ecosystem. It allows IndLan programs to seamlessly leverage real, installed Python libraries for Data Science, Machine Learning, AI, Mathematics, Visualization, File Processing, and Scientific Computing.

Made by Bhavya S Solanki.

Key Features

• Generic Python Bridge: Seamlessly import and call any Python package (pandas, numpy, scikit-learn, matplotlib, seaborn, tensorflow, torch, joblib, openpyxl, sqlite3, etc.).
• Bilingual English & Hindi APIs: Write code with English or Hindi keywords and aliases interchangeably (e.g. import pandas as pd or aayat pandas ke_roop_mein pd).
• Real Python Objects: Transparent bridging keeps objects as real pandas.DataFrame, numpy.ndarray, torch.Tensor, sklearn models, etc.
• Universal & Library-Specific Hindi Aliases: model.sikhao(X, y) (fit), model.bhavishyavani(X) (predict), np.sarni() (array), pd.csv_padho() (read_csv), plt.rekha() (plot), ganit.vargmool() (sqrt), etc.
• Keyword Arguments: Full support for Python-style keyword arguments (train_test_split(X, y, test_size=0.2)).
• Multiple Target Unpacking: maano X_train, X_test, y_train, y_test = train_test_split(...)
• Missing Package Diagnostics: Actionable diagnostics recommending the exact pip command and indlan extras when a package is not installed.
• Native Data Science Helpers: Built-in convenient csv_padho, csv_likho, csv_jodo, csv_badlo, csv_chhano, json_padho, json_likho, excel_padho, excel_likho.

Installation & Extras

Basic

pip install indlan

Data Science

pip install "indlan[data]"

Machine Learning

pip install "indlan[ml]"

AI / Deep Learning

pip install "indlan[ai]"

Complete Ecosystem

pip install "indlan[full]"

CLI Usage

indlan                             # Start interactive REPL
indlan program.ind                 # Run an IndLan program
indlan run program.ind             # Run an IndLan program
indlan repl                        # Start REPL
indlan --debug program.ind         # Run with Python traceback debugging
indlan --version                   # Show version
indlan --help                      # Show help

Import Syntax

English

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from tensorflow import keras

Hindi

aayat pandas ke_roop_mein pd
aayat numpy ke_roop_mein np
aayat matplotlib.pyplot ke_roop_mein plt
se sklearn.ensemble aayat RandomForestClassifier
se tensorflow aayat keras

Complete Data Science Example

Hindi Style

aayat pandas ke_roop_mein pd
aayat numpy ke_roop_mein np
aayat matplotlib.pyplot ke_roop_mein plt

se sklearn.model_selection aayat train_test_split
se sklearn.ensemble aayat RandomForestClassifier
se sklearn.metrics aayat satikta_ank

maano data = pd.csv_padho("students.csv")
chhap(data.shuru_ke(5))
chhap("Aakar:", data.shape)

maano X = np.sarni([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9]])
maano y = np.sarni([0, 0, 0, 0, 1, 1, 1, 1])

maano X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.25,
    random_state=42
)

maano model = RandomForestClassifier(random_state=42)
model.sikhao(X_train, y_train)

maano prediction = model.bhavishyavani(X_test)
chhap("Bhavishyavani:", prediction)
chhap("Satikta:", satikta_ank(y_test, prediction))

plt.rekha(prediction)
plt.sheershak("Model Prediction")
plt.jaal(true)
plt.dikhao()

English Style

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

data = pd.read_csv("students.csv")
print(data.head(5))
print("Shape:", data.shape)

X = np.array([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9]])
y = np.array([0, 0, 0, 0, 1, 1, 1, 1])

X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.25,
    random_state=42
)

model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

prediction = model.predict(X_test)
print("Prediction:", prediction)
print("Accuracy:", accuracy_score(y_test, prediction))

plt.plot(prediction)
plt.title("Model Prediction")
plt.grid(true)
plt.show()

Hindi-English Keyword & Method Reference

Keywords

Universal Method Aliases

Native File Helpers