app.py

import streamlit as st
import numpy as np

st.sidebar.title("NumPy Demo")

# Array creation routines
st.sidebar.header("Array creation routines")
st.sidebar.write("np.zeros(5):", np.zeros(5))
st.sidebar.write("np.ones((2, 3)):", np.ones((2, 3)))
st.sidebar.write("np.arange(0, 10, 2):", np.arange(0, 10, 2))
st.sidebar.write("np.linspace(0, 1, 5):", np.linspace(0, 1, 5))
st.sidebar.write("np.eye(3):", np.eye(3))

# Array manipulation routines
st.sidebar.header("Array manipulation routines")
arr = np.array([[1, 2], [3, 4]])
st.sidebar.write("arr.flatten():", arr.flatten())
st.sidebar.write("np.transpose(arr):", np.transpose(arr))
st.sidebar.write("np.rot90(arr):", np.rot90(arr))

# Binary operations
st.sidebar.header("Binary operations")
x = np.array([1, 2, 3])
y = np.array([4, 5, 6])
st.sidebar.write("np.add(x, y):", np.add(x, y))
st.sidebar.write("np.subtract(x, y):", np.subtract(x, y))
st.sidebar.write("np.multiply(x, y):", np.multiply(x, y))

# String operations
st.sidebar.header("String operations")
st.sidebar.write("np.char.add(['hello', 'world'], ['!', '?']):", np.char.add(['hello', 'world'], ['!', '?']))
st.sidebar.write("np.char.upper('numpy'):", np.char.upper('numpy'))
st.sidebar.write("np.char.replace('numpy', 'py', 'ython'):", np.char.replace('numpy', 'py', 'ython'))

# C-Types Foreign Function Interface (numpy.ctypeslib)
st.sidebar.header("C-Types Foreign Function Interface (numpy.ctypeslib)")
# Omitted for simplicity

# Datetime Support Functions
st.sidebar.header("Datetime Support Functions")
st.sidebar.write("np.datetime64('2023-02-21'):", np.datetime64('2023-02-21'))
st.sidebar.write("np.datetime64('2023-02-21 12:00:00'):", np.datetime64('2023-02-21 12:00:00'))

# Data type routines
st.sidebar.header("Data type routines")
st.sidebar.write("np.dtype('float64'):", np.dtype('float64'))
st.sidebar.write("np.issubdtype(np.float64, np.number):", np.issubdtype(np.float64, np.number))

# Optionally SciPy-accelerated routines (numpy.dual)
st.sidebar.header("Optionally SciPy-accelerated routines (numpy.dual)")
# Omitted for simplicity

# Mathematical functions with automatic domain
st.sidebar.header("Mathematical functions with automatic domain")
st.sidebar.write("np.sqrt(-1):", np.sqrt(-1))
st.sidebar.write("np.log(0):", np.log(0))

# Functional programming
st.sidebar.header("Functional programming")
st.sidebar.write("np.vectorize(np.square)([1, 2, 3]):", np.vectorize(np.square)([1, 2, 3]))

# NumPy-specific help functions
st.sidebar.header("NumPy-specific help functions")
st.sidebar.write("np.info(np.add):", np.info(np.add))

# Linear algebra (numpy.linalg)
st.sidebar.header("Linear algebra (numpy.linalg)")
mat = np.array([[1, 2], [3, 4]])
st.sidebar.write("np.linalg.inv(mat):", np.linalg.inv(mat))
st.sidebar.write("np.linalg.eig(mat):", np.linalg.eig(mat))

# Logic functions
st.sidebar.header("Logic functions")
x = np.array([1, 2, 3])
y = np.array([2, 2, 2])
st.sidebar.write("np.logical_and(x > 1, y < 3):", np.logical_and(x > 1, y < 3))
st.sidebar.write("np.logical_or(x > 2, y < 2):", np.logical_or(x > 2, y < 2))
st.sidebar.write("np.logical_not(x > 2):", np.logical_not(x > 2))

# Mathematical functions
st.sidebar.header("Mathematical functions")
x = np.array([0, 1, 2])
st.sidebar.write("np.exp(x):", np.exp(x))
st.sidebar.write("np.sin(x):", np.sin(x))
st.sidebar.write("np.arctan(x):", np.arctan(x))

# Miscellaneous routines
st.sidebar.header("Miscellaneous routines")
st.sidebar.write("np.percentile([1, 2, 3, 4, 5], 50):", np.percentile([1, 2, 3, 4, 5], 50))
st.sidebar.write("np.histogram([1, 2, 1], bins=[0, 1, 2, 3]):", np.histogram([1, 2, 1], bins=[0, 1, 2, 3]))

# Polynomials
st.sidebar.header("Polynomials")
st.sidebar.write("np.poly1d([1, 2, 3])(4):", np.poly1d([1, 2, 3])(4))

# Random sampling (numpy.random)
st.sidebar.header("Random sampling (numpy.random)")
st.sidebar.write("np.random.rand(3, 2):", np.random.rand(3, 2))
st.sidebar.write("np.random.normal(size=(2, 2)):", np.random.normal(size=(2, 2)))

#Set routines
st.sidebar.header("Set routines")
x = np.array([1, 2, 3, 4])
y = np.array([3, 4, 5, 6])
st.sidebar.write("np.intersect1d(x, y):", np.intersect1d(x, y))
st.sidebar.write("np.union1d(x, y):", np.union1d(x, y))
st.sidebar.write("np.setdiff1d(x, y):", np.setdiff1d(x, y))

#Sorting, searching, and counting
st.sidebar.header("Sorting, searching, and counting")
x = np.array([3, 1, 4, 1, 5, 9, 2, 6, 5, 3])
st.sidebar.write("np.sort(x):", np.sort(x))
st.sidebar.write("np.argsort(x):", np.argsort(x))
st.sidebar.write("np.where(x == 5):", np.where(x == 5))
st.sidebar.write("np.count_nonzero(x > 3):", np.count_nonzero(x > 3))

# Statistics
st.sidebar.header("Statistics")
x = np.array([3, 1, 4, 1, 5, 9, 2, 6, 5, 3])
st.sidebar.write("np.mean(x):", np.mean(x))
st.sidebar.write("np.std(x):", np.std(x))
st.sidebar.write("np.median(x):", np.median(x))