{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"\n",
"[*NBBinder test on a collection of notebooks about some thermodynamic properperties of water*](https://github.com/rmsrosa/nbbinder)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"\n",
"\n",
"\n",
"
\n",
" \n",
" \n",
" temp \n",
" density \n",
" viscosity \n",
" \n",
" \n",
" Temperature (C) \n",
" Density (g/cm^3) \n",
" Viscosity (cm^2/s) \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0 \n",
" 0.9999 \n",
" 0.01787 \n",
" \n",
" \n",
" 1 \n",
" 5 \n",
" 1.0000 \n",
" 1.51400 \n",
" \n",
" \n",
" 2 \n",
" 10 \n",
" 0.9997 \n",
" 1.30400 \n",
" \n",
" \n",
" 3 \n",
" 15 \n",
" 0.9991 \n",
" 1.13800 \n",
" \n",
" \n",
" 4 \n",
" 20 \n",
" 0.9982 \n",
" 1.00400 \n",
" \n",
" \n",
" 5 \n",
" 25 \n",
" 0.9971 \n",
" 0.89400 \n",
" \n",
" \n",
" 6 \n",
" 30 \n",
" 0.9957 \n",
" 0.80200 \n",
" \n",
" \n",
" 7 \n",
" 35 \n",
" 0.9941 \n",
" 0.72500 \n",
" \n",
" \n",
" 8 \n",
" 40 \n",
" 0.9923 \n",
" 0.65900 \n",
" \n",
" \n",
" 9 \n",
" 50 \n",
" 0.9881 \n",
" 0.55400 \n",
" \n",
" \n",
" 10 \n",
" 60 \n",
" 0.9832 \n",
" 0.47500 \n",
" \n",
" \n",
" 11 \n",
" 70 \n",
" 0.9778 \n",
" 0.41400 \n",
" \n",
" \n",
" 12 \n",
" 80 \n",
" 0.9718 \n",
" 0.36600 \n",
" \n",
" \n",
" 13 \n",
" 90 \n",
" 0.9653 \n",
" 0.32700 \n",
" \n",
" \n",
" 14 \n",
" 100 \n",
" 0.9584 \n",
" 0.29500 \n",
" \n",
" \n",
"
\n",
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax1 = plt.subplots(figsize=(10,5))\n",
"\n",
"color = 'tab:blue'\n",
"ax1.set_xlabel(water_pd['temp'].columns[0], fontsize=12)\n",
"ax1.set_ylabel(water_pd['density'].columns[0], color=color, fontsize=12)\n",
"ax1.plot(water_pd['temp'], water_pd['density'], 'o', color=color)\n",
"ax1.tick_params(axis='y', labelcolor=color)\n",
"\n",
"ax2 = ax1.twinx() \n",
"\n",
"color = 'tab:red'\n",
"ax2.set_ylabel(water_pd['viscosity'].columns[0], color=color, fontsize=12)\n",
"ax2.plot(water_pd['temp'], water_pd['viscosity'], 'o', color=color)\n",
"ax2.tick_params(axis='y', labelcolor=color)\n",
"\n",
"plt.title('Temperature-dependency of density and viscosity of pure water at 1 atm', \n",
" fontsize=14)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"\n",
"\n",
"---\n",
"[<- Introduction](01.00-Introduction.ipynb) | [Water Contents](00.00-Water_Contents.ipynb) | [References](BA.00-References.ipynb) | [Low-Dimensional Fittings ->](03.00-Low_Dim_Fittings.ipynb)"
]
}
],
"metadata": {
"file_extension": ".py",
"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": "3.7.3"
},
"mimetype": "text/x-python",
"name": "python",
"npconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": 3
},
"nbformat": 4,
"nbformat_minor": 4
}
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\" "\\"Open"){kind=icon}
![\"Open](\"https://mybinder.org/badge.svg\" "\\"Open"){kind=icon}
![\"View](\"https://img.shields.io/badge/view%20in-nbviewer-orange\" "\\"View"){kind=icon}
![\"View](\"https://img.shields.io/badge/view-slides-darkgreen\" "\\"View"){kind=icon}
"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"\n",
"[<- Introduction](01.00-Introduction.ipynb) | [Water Contents](00.00-Water_Contents.ipynb) | [References](BA.00-References.ipynb) | [Low-Dimensional Fittings ->](03.00-Low_Dim_Fittings.ipynb)\n",
"\n",
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Reading the Data\n",
"\n",
"A table with the variation of density and viscosity in terms of the temperature, at a fixed pressure of $1$ atmosphere, is available in [Batchelor (2000)](BA.00-References.ipynb). The data has been digitized and saved into a local `csv` file. Here we load the table from the file and view and plot the data."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Importing the libraries\n",
"\n",
"First we import the libraries used in this particular notebook."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Using pandas\n",
"\n",
"The data has been digitized to the local file `water.csv`. An easy way to retrieve it is with the [pandas.read_csv()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) function of the `pandas` library:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"water_pd = pd.read_csv('water.csv', header=[0,1])"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Viewing the data with pandas\n",
"\n",
"The data is diplayed nicely with pandas:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
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