{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"# Motivation"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Why Python?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"> Python is an unusual case for being both one of the **most visited** tags on Stack Overflow and one of the **fastest-growing** ones. (Incidentally, it is also accelerating! Its year-over-year growth has become faster each year since 2013). Source: [StackOverflow Blog](https://stackoverflow.blog/2017/09/06/incredible-growth-python/)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Python...\n",
"- is beginner friendly\n",
"- flexible\n",
"- readable\n",
"- has a big onliny community\n",
"- is a first-class tool for scientific computing tasks\n",
"- is used in Remote Sensing, Machine Learning, Big Data Analysis, Image Processing , Data Visualization\n",
"- is the 2nd most demanded programming skill (in the US)\n",
"- is the 2nd best paid programming skill (> 105'000$ in the US)\n",
"- is heavily used at large companies like Google & Facebook but also at NASA, ESA, EUMETSAT, etc."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Aim of the course\n",
"\n",
"At the end of this course you will be able ..."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "skip"
},
"tags": [
"hidecode"
]
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import xarray as xr\n",
"import satpy\n",
"import matplotlib.pyplot as plt\n",
"from scipy.optimize import curve_fit\n",
"from matplotlib.pyplot import rcParams\n",
"import warnings\n",
"\n",
"rcParams['figure.figsize'] = 8,6\n",
"rcParams['font.size'] = 14\n",
"\n",
"def func(x,a):\n",
" return np.exp(x) + a\n",
"\n",
"xdata = np.linspace(0, 4, 50)\n",
"y = func(xdata,5)\n",
"y_noise = 5.2 * np.random.normal(size=xdata.size)\n",
"ydata = y + y_noise\n",
"\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"... to work with the basic concepts of Python:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n",
"fünf\n",
"6\n",
"7\n",
"8\n",
"9\n"
]
}
],
"source": [
"for i in range(10):\n",
" if i==5:\n",
" print(\"fünf\")\n",
" else:\n",
" print(i)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"... to read, interpret and manipulate your scientific data with the standard Python tools for data science (numpy, scipy, pandas):"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
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