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<!DOCTYPE html>
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<title>Stata in Python Notebook - Economic Growth and Comparative Development</title>
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<meta name="description" content="in a Notebook You can work with Stata in a Python notebook by using the package ipystata. Just like r2py, which allows us to use R in Python, we can now use both (or if you want all three!) programming languages in one notebook. Setup¶ Let's start by importing all the packages we want to use." />
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<meta property="og:description" content="in a Notebook You can work with Stata in a Python notebook by using the package ipystata. Just like r2py, which allows us to use R in Python, we can now use both (or if you want all three!) programming languages in one notebook. Setup¶ Let's start by importing all the packages we want to use."/>
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Stata in Python Notebook
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<h1><img src="https://www.stata.com/includes/images/stata-fb.jpg" alt="Stata" width="200" /> in a <img src="https://www.python.org/static/community_logos/python-logo-inkscape.svg" alt="Python" width="200/"> <img src="https://raw.githubusercontent.com/adebar/awesome-jupyter/master/logo.png" alt="Jupyter" width="250/"> Notebook</h1>
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<p>You can work with <code>Stata</code> in a <code>Python</code> notebook by using the package <code>ipystata</code>. Just like <code>r2py</code>, which allows us to use <code>R</code> in <code>Python</code>, we can now use both (or if you want all three!) programming languages in one notebook.</p>
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<h1 id="Setup">Setup<a class="anchor-link" href="#Setup">¶</a></h1>
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<p>Let's start by importing all the packages we want to use.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">ipystata</span>
<span class="o">%</span><span class="k">pylab</span> --no-import-all
<span class="o">%</span><span class="k">matplotlib</span> inline
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<pre>IPyStata is loaded in batch mode.
Using matplotlib backend: MacOSX
Populating the interactive namespace from numpy and matplotlib
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<h1 id="%%stata-magic">%%stata magic<a class="anchor-link" href="#%%stata-magic">¶</a></h1><p>In order to use <code>ipystata</code> you will need to use the <code>%%stata</code> magic. Let's see the help for it.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="k">stata</span>?
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<pre><span class="ansi-red-fg">Docstring:</span>
::
%stata [-i INPUT] [-d DATA] [-o OUTPUT] [-np] [-cwd CHANGEWD] [-gr] [-os] [-cl]
optional arguments:
-i INPUT, --input INPUT
This is an input argument.
-d DATA, --data DATA This is the data input argument.
-o OUTPUT, --output OUTPUT
This is the output argument.
-np, --noprint Force the magic to not return an output.
-cwd CHANGEWD, --changewd CHANGEWD
Define a working directory for the Stata session.
-gr, --graph This will classify the Stata cell as one that returns a graph.
-os, --openstata Open Stata.
-cl, --close Tries (!) to auto-close Stata
<span class="ansi-red-fg">File:</span> ~/anaconda3/envs/GeoPython38env/lib/python3.8/site-packages/ipystata/ipystata_magic_batch.py
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<h1 id="First-example">First example<a class="anchor-link" href="#First-example">¶</a></h1><p>Let's run some commands in <code>Stata</code> from this notebook. Let's run the same code as in the <a href="./Stata%20Notebook%20Example.ipynb">Stata Notebook Example</a>. To do so, we will use the <code>%%stata</code> magic.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="k">stata</span>
sysuse auto.dta
summ
desc
reg price mpg rep78 headroom trunk weight length turn displacement gear_ratio foreign, r
scatter price mpg, mlabel(make)
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<pre>
(1978 Automobile Data)
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
make | 0
price | 74 6165.257 2949.496 3291 15906
mpg | 74 21.2973 5.785503 12 41
rep78 | 69 3.405797 .9899323 1 5
headroom | 74 2.993243 .8459948 1.5 5
-------------+---------------------------------------------------------
trunk | 74 13.75676 4.277404 5 23
weight | 74 3019.459 777.1936 1760 4840
length | 74 187.9324 22.26634 142 233
turn | 74 39.64865 4.399354 31 51
displacement | 74 197.2973 91.83722 79 425
-------------+---------------------------------------------------------
gear_ratio | 74 3.014865 .4562871 2.19 3.89
foreign | 74 .2972973 .4601885 0 1
Contains data from /Applications/Stata/ado/base/a/auto.dta
obs: 74 1978 Automobile Data
vars: 12 13 Apr 2018 17:45
(_dta has notes)
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
storage display value
variable name type format label variable label
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
make str18 %-18s Make and Model
price int %8.0gc Price
mpg int %8.0g Mileage (mpg)
rep78 int %8.0g Repair Record 1978
headroom float %6.1f Headroom (in.)
trunk int %8.0g Trunk space (cu. ft.)
weight int %8.0gc Weight (lbs.)
length int %8.0g Length (in.)
turn int %8.0g Turn Circle (ft.)
displacement int %8.0g Displacement (cu. in.)
gear_ratio float %6.2f Gear Ratio
foreign byte %8.0g origin Car type
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Sorted by: foreign
Linear regression Number of obs = 69
F(10, 58) = 11.47
Prob > F = 0.0000
R-squared = 0.5989
Root MSE = 1997.3
------------------------------------------------------------------------------
| Robust
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mpg | -21.80518 84.00518 -0.26 0.796 -189.9598 146.3495
rep78 | 184.7935 337.0935 0.55 0.586 -489.9724 859.5594
headroom | -635.4921 251.3987 -2.53 0.014 -1138.721 -132.263
trunk | 71.49929 70.18603 1.02 0.313 -68.9933 211.9919
weight | 4.521161 1.963781 2.30 0.025 .590227 8.452096
length | -76.49101 51.40168 -1.49 0.142 -179.3826 26.40062
turn | -114.2777 122.2631 -0.93 0.354 -359.0139 130.4585
displacement | 11.54012 7.065004 1.63 0.108 -2.602017 25.68227
gear_ratio | -318.6479 1016.632 -0.31 0.755 -2353.658 1716.362
foreign | 3334.848 988.7149 3.37 0.001 1355.721 5313.976
_cons | 9789.494 8240.462 1.19 0.240 -6705.583 26284.57
------------------------------------------------------------------------------
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<p>Notice that it returned everything except the graph. To be able to get the graph we need to provide the option <code>-s graph_session</code> to the <code>%%stata</code> magic.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="k">stata</span> -gr
sysuse auto.dta
summ
desc
reg price mpg rep78 headroom trunk weight length turn displacement gear_ratio foreign, r
scatter price mpg, mlabel(make)
</pre></div>
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(1978 Automobile Data)
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
make | 0
price | 74 6165.257 2949.496 3291 15906
mpg | 74 21.2973 5.785503 12 41
rep78 | 69 3.405797 .9899323 1 5
headroom | 74 2.993243 .8459948 1.5 5
-------------+---------------------------------------------------------
trunk | 74 13.75676 4.277404 5 23
weight | 74 3019.459 777.1936 1760 4840
length | 74 187.9324 22.26634 142 233
turn | 74 39.64865 4.399354 31 51
displacement | 74 197.2973 91.83722 79 425
-------------+---------------------------------------------------------
gear_ratio | 74 3.014865 .4562871 2.19 3.89
foreign | 74 .2972973 .4601885 0 1
Contains data from /Applications/Stata/ado/base/a/auto.dta
obs: 74 1978 Automobile Data
vars: 12 13 Apr 2018 17:45
(_dta has notes)
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
storage display value
variable name type format label variable label
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
make str18 %-18s Make and Model
price int %8.0gc Price
mpg int %8.0g Mileage (mpg)
rep78 int %8.0g Repair Record 1978
headroom float %6.1f Headroom (in.)
trunk int %8.0g Trunk space (cu. ft.)
weight int %8.0gc Weight (lbs.)
length int %8.0g Length (in.)
turn int %8.0g Turn Circle (ft.)
displacement int %8.0g Displacement (cu. in.)
gear_ratio float %6.2f Gear Ratio
foreign byte %8.0g origin Car type
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Sorted by: foreign
Linear regression Number of obs = 69
F(10, 58) = 11.47
Prob > F = 0.0000
R-squared = 0.5989
Root MSE = 1997.3
------------------------------------------------------------------------------
| Robust
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mpg | -21.80518 84.00518 -0.26 0.796 -189.9598 146.3495
rep78 | 184.7935 337.0935 0.55 0.586 -489.9724 859.5594
headroom | -635.4921 251.3987 -2.53 0.014 -1138.721 -132.263
trunk | 71.49929 70.18603 1.02 0.313 -68.9933 211.9919
weight | 4.521161 1.963781 2.30 0.025 .590227 8.452096
length | -76.49101 51.40168 -1.49 0.142 -179.3826 26.40062
turn | -114.2777 122.2631 -0.93 0.354 -359.0139 130.4585
displacement | 11.54012 7.065004 1.63 0.108 -2.602017 25.68227
gear_ratio | -318.6479 1016.632 -0.31 0.755 -2353.658 1716.362
foreign | 3334.848 988.7149 3.37 0.001 1355.721 5313.976
_cons | 9789.494 8240.462 1.19 0.240 -6705.583 26284.57
------------------------------------------------------------------------------
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<p>Looks like there are issues preventing <code>Stata</code> to pass the figure back to <code>Jupyter</code>. Nonetheless, we can save it in <code>Stata</code> and open it here.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="k">stata</span> -gr
sysuse auto.dta
summ
desc
reg price mpg rep78 headroom trunk weight length turn displacement gear_ratio foreign, r
scatter price mpg, mlabel(make)
graph export "./graphs/price-mpg.png", replace
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(1978 Automobile Data)
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
make | 0
price | 74 6165.257 2949.496 3291 15906
mpg | 74 21.2973 5.785503 12 41
rep78 | 69 3.405797 .9899323 1 5
headroom | 74 2.993243 .8459948 1.5 5
-------------+---------------------------------------------------------
trunk | 74 13.75676 4.277404 5 23
weight | 74 3019.459 777.1936 1760 4840
length | 74 187.9324 22.26634 142 233
turn | 74 39.64865 4.399354 31 51
displacement | 74 197.2973 91.83722 79 425
-------------+---------------------------------------------------------
gear_ratio | 74 3.014865 .4562871 2.19 3.89
foreign | 74 .2972973 .4601885 0 1
Contains data from /Applications/Stata/ado/base/a/auto.dta
obs: 74 1978 Automobile Data
vars: 12 13 Apr 2018 17:45
(_dta has notes)
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
storage display value
variable name type format label variable label
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
make str18 %-18s Make and Model
price int %8.0gc Price
mpg int %8.0g Mileage (mpg)
rep78 int %8.0g Repair Record 1978
headroom float %6.1f Headroom (in.)
trunk int %8.0g Trunk space (cu. ft.)
weight int %8.0gc Weight (lbs.)
length int %8.0g Length (in.)
turn int %8.0g Turn Circle (ft.)
displacement int %8.0g Displacement (cu. in.)
gear_ratio float %6.2f Gear Ratio
foreign byte %8.0g origin Car type
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Sorted by: foreign
Linear regression Number of obs = 69
F(10, 58) = 11.47
Prob > F = 0.0000
R-squared = 0.5989
Root MSE = 1997.3
------------------------------------------------------------------------------
| Robust
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mpg | -21.80518 84.00518 -0.26 0.796 -189.9598 146.3495
rep78 | 184.7935 337.0935 0.55 0.586 -489.9724 859.5594
headroom | -635.4921 251.3987 -2.53 0.014 -1138.721 -132.263
trunk | 71.49929 70.18603 1.02 0.313 -68.9933 211.9919
weight | 4.521161 1.963781 2.30 0.025 .590227 8.452096
length | -76.49101 51.40168 -1.49 0.142 -179.3826 26.40062
turn | -114.2777 122.2631 -0.93 0.354 -359.0139 130.4585
displacement | 11.54012 7.065004 1.63 0.108 -2.602017 25.68227
gear_ratio | -318.6479 1016.632 -0.31 0.755 -2353.658 1716.362
foreign | 3334.848 988.7149 3.37 0.001 1355.721 5313.976
_cons | 9789.494 8240.462 1.19 0.240 -6705.583 26284.57
------------------------------------------------------------------------------
(file /Users/ozak/Dropbox/GitHub/econgrowth.github.io-src/ghpages/content/notebooks/graphs/price-mpg.png written in PNG format)
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<p>Let's import the figure to our notebook.
<img src="./graphs/price-mpg.png" alt="price-mpg"></p>
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<h1 id="Moving-data-between-Stata-and-Python">Moving data between Stata and Python<a class="anchor-link" href="#Moving-data-between-Stata-and-Python">¶</a></h1>
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<p>As we have seen <code>Python</code> is very powerful for data munging and cleaning. Also, we have seen that figures may look much nicer. But, since we already know <code>Stata</code> for econometric analyses, let's use both languages to get the best of each. We can do this by passing additional options to <code>%%stata</code>. First, let's get the data from <code>auto.dta</code> from <code>Stata</code> as a <code>pandas</code> dataframe.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="k">stata</span> -o car_df
sysuse auto.dta
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(1978 Automobile Data)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">car_df</span>
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<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>make</th>
<th>price</th>
<th>mpg</th>
<th>rep78</th>
<th>headroom</th>
<th>trunk</th>
<th>weight</th>
<th>length</th>
<th>turn</th>
<th>displacement</th>
<th>gear_ratio</th>
<th>foreign</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>AMC Concord</td>
<td>4099</td>
<td>22</td>
<td>3.0</td>
<td>2.5</td>
<td>11</td>
<td>2930</td>
<td>186</td>
<td>40</td>
<td>121</td>
<td>3.58</td>
<td>Domestic</td>
</tr>
<tr>
<th>1</th>
<td>AMC Pacer</td>
<td>4749</td>
<td>17</td>
<td>3.0</td>
<td>3.0</td>
<td>11</td>
<td>3350</td>
<td>173</td>
<td>40</td>
<td>258</td>
<td>2.53</td>
<td>Domestic</td>
</tr>
<tr>
<th>2</th>
<td>AMC Spirit</td>
<td>3799</td>
<td>22</td>
<td>NaN</td>
<td>3.0</td>
<td>12</td>
<td>2640</td>
<td>168</td>
<td>35</td>
<td>121</td>
<td>3.08</td>
<td>Domestic</td>
</tr>
<tr>
<th>3</th>
<td>Buick Century</td>
<td>4816</td>
<td>20</td>
<td>3.0</td>
<td>4.5</td>
<td>16</td>
<td>3250</td>
<td>196</td>
<td>40</td>
<td>196</td>
<td>2.93</td>
<td>Domestic</td>
</tr>
<tr>
<th>4</th>
<td>Buick Electra</td>
<td>7827</td>
<td>15</td>
<td>4.0</td>
<td>4.0</td>
<td>20</td>
<td>4080</td>
<td>222</td>
<td>43</td>
<td>350</td>
<td>2.41</td>
<td>Domestic</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>69</th>
<td>VW Dasher</td>
<td>7140</td>
<td>23</td>
<td>4.0</td>
<td>2.5</td>
<td>12</td>
<td>2160</td>
<td>172</td>
<td>36</td>
<td>97</td>
<td>3.74</td>
<td>Foreign</td>
</tr>
<tr>
<th>70</th>
<td>VW Diesel</td>
<td>5397</td>
<td>41</td>
<td>5.0</td>
<td>3.0</td>
<td>15</td>
<td>2040</td>
<td>155</td>
<td>35</td>
<td>90</td>
<td>3.78</td>
<td>Foreign</td>
</tr>
<tr>
<th>71</th>
<td>VW Rabbit</td>
<td>4697</td>
<td>25</td>
<td>4.0</td>
<td>3.0</td>
<td>15</td>
<td>1930</td>
<td>155</td>
<td>35</td>
<td>89</td>
<td>3.78</td>
<td>Foreign</td>
</tr>
<tr>
<th>72</th>
<td>VW Scirocco</td>
<td>6850</td>
<td>25</td>
<td>4.0</td>
<td>2.0</td>
<td>16</td>
<td>1990</td>
<td>156</td>
<td>36</td>
<td>97</td>
<td>3.78</td>
<td>Foreign</td>
</tr>
<tr>
<th>73</th>
<td>Volvo 260</td>
<td>11995</td>
<td>17</td>
<td>5.0</td>
<td>2.5</td>
<td>14</td>
<td>3170</td>
<td>193</td>
<td>37</td>
<td>163</td>
<td>2.98</td>
<td>Foreign</td>
</tr>
</tbody>
</table>
<p>74 rows × 12 columns</p>
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<h2 id="Some-analyses-in-Python">Some analyses in Python<a class="anchor-link" href="#Some-analyses-in-Python">¶</a></h2><p>Now that we have the data in <code>python</code> we can do some analyses, merge with other datasets, or create some plots.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Import matplotlib</span>
<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
<span class="c1"># Import seaborn</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">()</span>
<span class="c1"># paths</span>
<span class="n">pathgraphs</span> <span class="o">=</span> <span class="s1">'./graphs/'</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Define our function to plot</span>
<span class="k">def</span> <span class="nf">ScatterPlot</span><span class="p">(</span><span class="n">dfin</span><span class="p">,</span> <span class="n">var0</span><span class="o">=</span><span class="s1">'mpg'</span><span class="p">,</span> <span class="n">var1</span><span class="o">=</span><span class="s1">'price'</span><span class="p">,</span> <span class="n">labelvar</span><span class="o">=</span><span class="s1">'make'</span><span class="p">,</span>
<span class="n">dx</span><span class="o">=</span><span class="mf">0.006125</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mf">0.006125</span><span class="p">,</span>
<span class="n">xlabel</span><span class="o">=</span><span class="s1">'Miles per Gallon'</span><span class="p">,</span>
<span class="n">ylabel</span><span class="o">=</span><span class="s1">'Price'</span><span class="p">,</span>
<span class="n">linelabel</span><span class="o">=</span><span class="s1">'Price'</span><span class="p">,</span>
<span class="n">filename</span><span class="o">=</span><span class="s1">'price-mpg.pdf'</span><span class="p">):</span>
<span class="sd">'''</span>
<span class="sd"> Plot the association between var0 and var in dataframe using labelvar for labels. </span>
<span class="sd"> '''</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">rc</span><span class="o">=</span><span class="p">{</span><span class="s1">'figure.figsize'</span><span class="p">:(</span><span class="mf">11.7</span><span class="p">,</span><span class="mf">8.27</span><span class="p">)})</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set_context</span><span class="p">(</span><span class="s2">"talk"</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">dfin</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="n">var0</span><span class="p">,</span> <span class="n">var1</span><span class="p">])</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Plot</span>
<span class="n">k</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">sns</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">var0</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">var1</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">linelabel</span><span class="p">)</span>
<span class="n">movex</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">var0</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">*</span> <span class="n">dx</span>
<span class="n">movey</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">var1</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">*</span> <span class="n">dy</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">var0</span><span class="p">][</span><span class="n">line</span><span class="p">]</span><span class="o">+</span><span class="n">movex</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="n">var1</span><span class="p">][</span><span class="n">line</span><span class="p">]</span><span class="o">+</span><span class="n">movey</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="n">labelvar</span><span class="p">][</span><span class="n">line</span><span class="p">],</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">ylabel</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="n">df</span><span class="p">[</span><span class="n">var0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="n">var0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="n">var1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mi">1000</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span> <span class="o">=</span> <span class="s1">'both'</span><span class="p">,</span> <span class="n">which</span> <span class="o">=</span> <span class="s1">'major'</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span> <span class="o">=</span> <span class="s1">'both'</span><span class="p">,</span> <span class="n">which</span> <span class="o">=</span> <span class="s1">'minor'</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">mpl</span><span class="o">.</span><span class="n">ticker</span><span class="o">.</span><span class="n">StrMethodFormatter</span><span class="p">(</span><span class="s1">'</span><span class="si">{x:,.0f}</span><span class="s1">'</span><span class="p">))</span>
<span class="c1">#ax.legend()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">pathgraphs</span> <span class="o">+</span> <span class="n">filename</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">'tight'</span><span class="p">)</span>
<span class="k">pass</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ScatterPlot</span><span class="p">(</span><span class="n">car_df</span><span class="p">)</span>
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<h3 id="Creating-some-data">Creating some data<a class="anchor-link" href="#Creating-some-data">¶</a></h3>