diff --git a/.ipynb_checkpoints/Histogram-checkpoint.ipynb b/.ipynb_checkpoints/Histogram-checkpoint.ipynb deleted file mode 100644 index 8a01da5..0000000 --- a/.ipynb_checkpoints/Histogram-checkpoint.ipynb +++ /dev/null @@ -1,258 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "
\n", - "

Prepared by Asif Bhat

\n", - " \n", - "

Data Visualization With Matplotlib

\n", - "\n", - "

Follow Me on - LinkedIn  Twitter  Instagram  Facebook

\n", - "
\n", - "\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Histogram" - ] - }, - { - "cell_type": "code", - "execution_count": 323, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.random.normal(size = 2000)\n", - "plt.hist(x, bins=40, color='yellowgreen')\n", - "plt.gca().set(title='Histogram', ylabel='Frequency')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 324, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.random.rand(2000)\n", - "plt.hist(x, bins=30 ,color='#D4AC0D')\n", - "plt.gca().set(title='Histogram', ylabel='Frequency')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 325, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Using Edge Color for readability\n", - "plt.figure(figsize=(10,8))\n", - "x = np.random.normal(size = 2000)\n", - "plt.hist(x, bins=40, color='yellowgreen' , edgecolor=\"#6A9662\")\n", - "plt.gca().set(title='Histogram', ylabel='Frequency')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Binning" - ] - }, - { - "cell_type": "code", - "execution_count": 326, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Binning\n", - "plt.figure(figsize=(10,8))\n", - "x = np.random.normal(size = 2000)\n", - "plt.hist(x, bins=30, color='yellowgreen' , edgecolor=\"#6A9662\")\n", - "plt.gca().set(title='Histogram', ylabel='Frequency')\n", - "plt.show()\n", - "\n", - "plt.figure(figsize=(10,8))\n", - "plt.hist(x, bins=20, color='yellowgreen' , edgecolor=\"#6A9662\")\n", - "plt.gca().set(title='Histogram', ylabel='Frequency')\n", - "plt.show()\n", - "\n", - "plt.figure(figsize=(10,8))\n", - "plt.hist(x, bins=10, color='yellowgreen' , edgecolor=\"#6A9662\")\n", - "plt.gca().set(title='Histogram', ylabel='Frequency')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plotting Multiple Histograms" - ] - }, - { - "cell_type": "code", - "execution_count": 329, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(8,11))\n", - "x = np.random.normal(-4,1,size = 800)\n", - "y = np.random.normal(0,1.5,size = 800)\n", - "z = np.random.normal(3.5,1,size = 800)\n", - "plt.hist(x, bins=30, color='yellowgreen' , alpha=0.6)\n", - "plt.hist(y, bins=30, color='#FF8F00' , alpha=0.6)\n", - "plt.hist(z, bins=30, color='blue' , alpha=0.6)\n", - "plt.gca().set(title='Histogram', ylabel='Frequency')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 330, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Using Histogram to plot a cumulative distribution function\n", - "plt.figure(figsize=(10,8))\n", - "x = np.random.rand(2000)\n", - "plt.hist(x, bins=30 ,color='#ffa41b' , edgecolor=\"#639a67\",cumulative=True)\n", - "plt.gca().set(title='Histogram', ylabel='Frequency')\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}