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python-cheatsheet
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Updated Pandas, Plotly

pull/167/head
Jure Šorn 1 year ago
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3 changed files with 41 additions and 31 deletions
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  1. 29
      README.md
  2. 30
      index.html
  3. 13
      parse.js

29
README.md
View File

@ -3184,16 +3184,16 @@ plt.show() # Displays the plot. Also plt.sav
``` ```
```python ```python
>>> sr = pd.Series([1, 2], index=['x', 'y'])
x 1
y 2
>>> sr = pd.Series([2, 3], index=['x', 'y'])
x 2
y 3
``` ```
```text ```text
+---------------+-------------+-------------+---------------+ +---------------+-------------+-------------+---------------+
| | 'sum' | ['sum'] | {'s': 'sum'} | | | 'sum' | ['sum'] | {'s': 'sum'} |
+---------------+-------------+-------------+---------------+ +---------------+-------------+-------------+---------------+
| sr.apply(…) | 3 | sum 3 | s 3 |
| sr.apply(…) | 5 | sum 5 | s 5 |
| sr.agg(…) | | | | | sr.agg(…) | | | |
+---------------+-------------+-------------+---------------+ +---------------+-------------+-------------+---------------+
``` ```
@ -3377,11 +3377,12 @@ c 7 8 6
``` ```
```python ```python
>>> gb = df.groupby('z')
x y z
3: a 1 2 3
6: b 4 5 6
c 7 8 6
>>> gb = df.groupby('z'); gb.apply(print)
x y z
a 1 2 3
x y z
b 4 5 6
c 7 8 6
``` ```
```text ```text
@ -3414,9 +3415,9 @@ c 7 8 6
Plotly Plotly
------ ------
```python ```python
# $ pip3 install plotly kaleido
from plotly.express import line
<Figure> = line(<DF>, x=<col_name>, y=<col_name>) # Or: line(x=<list>, y=<list>)
# $ pip3 install pandas plotly kaleido
import pandas as pd, plotly.express as ex
<Figure> = ex.line(<DF>, x=<col_name>, y=<col_name>) # Or: ex.line(x=<list>, y=<list>)
<Figure>.update_layout(margin=dict(t=0, r=0, b=0, l=0), …) # `paper_bgcolor='rgb(0, 0, 0)'`. <Figure>.update_layout(margin=dict(t=0, r=0, b=0, l=0), …) # `paper_bgcolor='rgb(0, 0, 0)'`.
<Figure>.write_html/json/image('<path>') # Also <Figure>.show(). <Figure>.write_html/json/image('<path>') # Also <Figure>.show().
``` ```
@ -3424,7 +3425,7 @@ from plotly.express import line
#### Displays a line chart of total coronavirus deaths per million grouped by continent: #### Displays a line chart of total coronavirus deaths per million grouped by continent:
![Covid Deaths](web/covid_deaths.png) ![Covid Deaths](web/covid_deaths.png)
<div id="2a950764-39fc-416d-97fe-0a6226a3095f" class="plotly-graph-div" style="height:340px; width:100%;"></div>
<div id="2a950764-39fc-416d-97fe-0a6226a3095f" class="plotly-graph-div" style="height:321px; width:100%;"></div>
```python ```python
covid = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv', covid = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv',
@ -3437,7 +3438,7 @@ df = df.groupby(['Continent_Name', 'date']).sum().reset_index()
df['Total Deaths per Million'] = df.total_deaths * 1e6 / df.population df['Total Deaths per Million'] = df.total_deaths * 1e6 / df.population
df = df[df.date > '2020-03-14'] df = df[df.date > '2020-03-14']
df = df.rename({'date': 'Date', 'Continent_Name': 'Continent'}, axis='columns') df = df.rename({'date': 'Date', 'Continent_Name': 'Continent'}, axis='columns')
line(df, x='Date', y='Total Deaths per Million', color='Continent').show()
ex.line(df, x='Date', y='Total Deaths per Million', color='Continent').show()
``` ```
#### Displays a multi-axis line chart of total coronavirus cases and changes in prices of Bitcoin, Dow Jones and gold: #### Displays a multi-axis line chart of total coronavirus cases and changes in prices of Bitcoin, Dow Jones and gold:

30
index.html
View File

@ -2599,14 +2599,14 @@ plt.show() <span class="hljs-comment"># Disp
&lt;Sr&gt; = &lt;Sr&gt;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;Sr&gt;.agg/transform/map(lambda &lt;el&gt;: &lt;el&gt;)</span> &lt;Sr&gt; = &lt;Sr&gt;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;Sr&gt;.agg/transform/map(lambda &lt;el&gt;: &lt;el&gt;)</span>
</code></pre></div> </code></pre></div>
<pre><code class="python language-python hljs"><span class="hljs-meta">&gt;&gt;&gt; </span>sr = pd.Series([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>], index=[<span class="hljs-string">'x'</span>, <span class="hljs-string">'y'</span>])
x <span class="hljs-number">1</span>
y <span class="hljs-number">2</span>
<pre><code class="python language-python hljs"><span class="hljs-meta">&gt;&gt;&gt; </span>sr = pd.Series([<span class="hljs-number">2</span>, <span class="hljs-number">3</span>], index=[<span class="hljs-string">'x'</span>, <span class="hljs-string">'y'</span>])
x <span class="hljs-number">2</span>
y <span class="hljs-number">3</span>
</code></pre> </code></pre>
<pre><code class="python hljs">┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓ <pre><code class="python hljs">┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓
┃ │ <span class="hljs-string">'sum'</span> │ [<span class="hljs-string">'sum'</span>] │ {<span class="hljs-string">'s'</span>: <span class="hljs-string">'sum'</span>} ┃ ┃ │ <span class="hljs-string">'sum'</span> │ [<span class="hljs-string">'sum'</span>] │ {<span class="hljs-string">'s'</span>: <span class="hljs-string">'sum'</span>} ┃
┠───────────────┼─────────────┼─────────────┼───────────────┨ ┠───────────────┼─────────────┼─────────────┼───────────────┨
┃ sr.apply(…) │ <span class="hljs-number">3</span> │ sum <span class="hljs-number">3</span> │ s <span class="hljs-number">3</span> ┃
┃ sr.apply(…) │ <span class="hljs-number">5</span> │ sum <span class="hljs-number">5</span> │ s <span class="hljs-number">5</span> ┃
┃ sr.agg(…) │ │ │ ┃ ┃ sr.agg(…) │ │ │ ┃
┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛ ┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛
@ -2756,12 +2756,12 @@ c <span class="hljs-number">7</span> <span class="hljs-number">8</span> <span
&lt;DF&gt; = &lt;GB&gt;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;GB&gt;.transform(lambda &lt;Sr&gt;: &lt;Sr&gt;)</span> &lt;DF&gt; = &lt;GB&gt;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;GB&gt;.transform(lambda &lt;Sr&gt;: &lt;Sr&gt;)</span>
</code></pre></div> </code></pre></div>
<pre><code class="python language-python hljs"><span class="hljs-meta">&gt;&gt;&gt; </span>gb = df.groupby(<span class="hljs-string">'z'</span>)
x y z
<span class="hljs-number">3</span>: a <span class="hljs-number">1</span> <span class="hljs-number">2</span> <span class="hljs-number">3</span>
<span class="hljs-number">6</span>: b <span class="hljs-number">4</span> <span class="hljs-number">5</span> <span class="hljs-number">6</span>
c <span class="hljs-number">7</span> <span class="hljs-number">8</span> <span class="hljs-number">6</span>
</code></pre>
<pre><code class="python language-python hljs"><span class="hljs-meta">&gt;&gt;&gt; </span>gb = df.groupby(<span class="hljs-string">'z'</span>); gb.apply(print)
x y z
a <span class="hljs-number">1</span> <span class="hljs-number">2</span> <span class="hljs-number">3</span>
x y z
b <span class="hljs-number">4</span> <span class="hljs-number">5</span> <span class="hljs-number">6</span>
c <span class="hljs-number">7</span> <span class="hljs-number">8</span> <span class="hljs-number">6</span></code></pre>
<pre><code class="python hljs">┏━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓ <pre><code class="python hljs">┏━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓
┃ │ <span class="hljs-string">'sum'</span> │ <span class="hljs-string">'rank'</span> │ [<span class="hljs-string">'rank'</span>] │ {<span class="hljs-string">'x'</span>: <span class="hljs-string">'rank'</span>} ┃ ┃ │ <span class="hljs-string">'sum'</span> │ <span class="hljs-string">'rank'</span> │ [<span class="hljs-string">'rank'</span>] │ {<span class="hljs-string">'x'</span>: <span class="hljs-string">'rank'</span>} ┃
┠─────────────────┼─────────────┼─────────────┼─────────────┼───────────────┨ ┠─────────────────┼─────────────┼─────────────┼─────────────┼───────────────┨
@ -2783,14 +2783,14 @@ c <span class="hljs-number">7</span> <span class="hljs-number">8</span> <span
</code></pre></div> </code></pre></div>
<div><h2 id="plotly"><a href="#plotly" name="plotly">#</a>Plotly</h2><pre><code class="python language-python hljs"><span class="hljs-comment"># $ pip3 install plotly kaleido</span>
<span class="hljs-keyword">from</span> plotly.express <span class="hljs-keyword">import</span> line
&lt;Figure&gt; = line(&lt;DF&gt;, x=&lt;col_name&gt;, y=&lt;col_name&gt;) <span class="hljs-comment"># Or: line(x=&lt;list&gt;, y=&lt;list&gt;)</span>
<div><h2 id="plotly"><a href="#plotly" name="plotly">#</a>Plotly</h2><pre><code class="python language-python hljs"><span class="hljs-comment"># $ pip3 install pandas plotly kaleido</span>
<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd, plotly.express <span class="hljs-keyword">as</span> ex
&lt;Figure&gt; = ex.line(&lt;DF&gt;, x=&lt;col_name&gt;, y=&lt;col_name&gt;) <span class="hljs-comment"># Or: ex.line(x=&lt;list&gt;, y=&lt;list&gt;)</span>
&lt;Figure&gt;.update_layout(margin=dict(t=<span class="hljs-number">0</span>, r=<span class="hljs-number">0</span>, b=<span class="hljs-number">0</span>, l=<span class="hljs-number">0</span>), …) <span class="hljs-comment"># `paper_bgcolor='rgb(0, 0, 0)'`.</span> &lt;Figure&gt;.update_layout(margin=dict(t=<span class="hljs-number">0</span>, r=<span class="hljs-number">0</span>, b=<span class="hljs-number">0</span>, l=<span class="hljs-number">0</span>), …) <span class="hljs-comment"># `paper_bgcolor='rgb(0, 0, 0)'`.</span>
&lt;Figure&gt;.write_html/json/image(<span class="hljs-string">'&lt;path&gt;'</span>) <span class="hljs-comment"># Also &lt;Figure&gt;.show().</span> &lt;Figure&gt;.write_html/json/image(<span class="hljs-string">'&lt;path&gt;'</span>) <span class="hljs-comment"># Also &lt;Figure&gt;.show().</span>
</code></pre></div> </code></pre></div>
<div><h4 id="displaysalinechartoftotalcoronavirusdeathspermilliongroupedbycontinent">Displays a line chart of total coronavirus deaths per million grouped by continent:</h4><p></p><div id="2a950764-39fc-416d-97fe-0a6226a3095f" class="plotly-graph-div" style="height:340px; width:100%;"></div><pre><code class="python language-python hljs">covid = pd.read_csv(<span class="hljs-string">'https://covid.ourworldindata.org/data/owid-covid-data.csv'</span>,
<div><h4 id="displaysalinechartoftotalcoronavirusdeathspermilliongroupedbycontinent">Displays a line chart of total coronavirus deaths per million grouped by continent:</h4><p></p><div id="2a950764-39fc-416d-97fe-0a6226a3095f" class="plotly-graph-div" style="height:321px; width:100%;"></div><pre><code class="python language-python hljs">covid = pd.read_csv(<span class="hljs-string">'https://covid.ourworldindata.org/data/owid-covid-data.csv'</span>,
usecols=[<span class="hljs-string">'iso_code'</span>, <span class="hljs-string">'date'</span>, <span class="hljs-string">'total_deaths'</span>, <span class="hljs-string">'population'</span>]) usecols=[<span class="hljs-string">'iso_code'</span>, <span class="hljs-string">'date'</span>, <span class="hljs-string">'total_deaths'</span>, <span class="hljs-string">'population'</span>])
continents = pd.read_csv(<span class="hljs-string">'https://gist.githubusercontent.com/stevewithington/20a69c0b6d2ff'</span> continents = pd.read_csv(<span class="hljs-string">'https://gist.githubusercontent.com/stevewithington/20a69c0b6d2ff'</span>
<span class="hljs-string">'846ea5d35e5fc47f26c/raw/country-and-continent-codes-list-csv.csv'</span>, <span class="hljs-string">'846ea5d35e5fc47f26c/raw/country-and-continent-codes-list-csv.csv'</span>,
@ -2800,7 +2800,7 @@ df = df.groupby([<span class="hljs-string">'Continent_Name'</span>, <span class=
df[<span class="hljs-string">'Total Deaths per Million'</span>] = df.total_deaths * <span class="hljs-number">1e6</span> / df.population df[<span class="hljs-string">'Total Deaths per Million'</span>] = df.total_deaths * <span class="hljs-number">1e6</span> / df.population
df = df[df.date &gt; <span class="hljs-string">'2020-03-14'</span>] df = df[df.date &gt; <span class="hljs-string">'2020-03-14'</span>]
df = df.rename({<span class="hljs-string">'date'</span>: <span class="hljs-string">'Date'</span>, <span class="hljs-string">'Continent_Name'</span>: <span class="hljs-string">'Continent'</span>}, axis=<span class="hljs-string">'columns'</span>) df = df.rename({<span class="hljs-string">'date'</span>: <span class="hljs-string">'Date'</span>, <span class="hljs-string">'Continent_Name'</span>: <span class="hljs-string">'Continent'</span>}, axis=<span class="hljs-string">'columns'</span>)
line(df, x=<span class="hljs-string">'Date'</span>, y=<span class="hljs-string">'Total Deaths per Million'</span>, color=<span class="hljs-string">'Continent'</span>).show()
ex.line(df, x=<span class="hljs-string">'Date'</span>, y=<span class="hljs-string">'Total Deaths per Million'</span>, color=<span class="hljs-string">'Continent'</span>).show()
</code></pre></div> </code></pre></div>

13
parse.js
View File

@ -294,6 +294,14 @@ const MARIO =
'<span class="hljs-keyword">if</span> __name__ == <span class="hljs-string">\'__main__\'</span>:\n' + '<span class="hljs-keyword">if</span> __name__ == <span class="hljs-string">\'__main__\'</span>:\n' +
' main()\n'; ' main()\n';
const PLOTLY =
'<span class="hljs-meta">&gt;&gt;&gt; </span>gb = df.groupby(<span class="hljs-string">\'z\'</span>); gb.apply(print)\n' +
' x y z\n' +
'a <span class="hljs-number">1</span> <span class="hljs-number">2</span> <span class="hljs-number">3</span>\n' +
' x y z\n' +
'b <span class="hljs-number">4</span> <span class="hljs-number">5</span> <span class="hljs-number">6</span>\n' +
'c <span class="hljs-number">7</span> <span class="hljs-number">8</span> <span class="hljs-number">6</span>';
const INDEX = const INDEX =
'<li><strong>Only available in the <a href="https://transactions.sendowl.com/products/78175486/4422834F/view">PDF</a>.</strong></li>\n' + '<li><strong>Only available in the <a href="https://transactions.sendowl.com/products/78175486/4422834F/view">PDF</a>.</strong></li>\n' +
'<li><strong>Ctrl+F / ⌘F is usually sufficient.</strong></li>\n' + '<li><strong>Ctrl+F / ⌘F is usually sufficient.</strong></li>\n' +
@ -538,13 +546,13 @@ const DIAGRAM_12_B =
'┗━━━━━━━━━━━┷━━━━━━━━━━━┷━━━━━━┷━━━━━━━━━━━┛\n'; '┗━━━━━━━━━━━┷━━━━━━━━━━━┷━━━━━━┷━━━━━━━━━━━┛\n';
const DIAGRAM_13_A = const DIAGRAM_13_A =
'| sr.apply(…) | 3 | sum 3 | s 3 |';
'| sr.apply(…) | 5 | sum 5 | s 5 |';
const DIAGRAM_13_B = const DIAGRAM_13_B =
"┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓\n" + "┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓\n" +
"┃ │ 'sum' │ ['sum'] │ {'s': 'sum'} ┃\n" + "┃ │ 'sum' │ ['sum'] │ {'s': 'sum'} ┃\n" +
"┠───────────────┼─────────────┼─────────────┼───────────────┨\n" + "┠───────────────┼─────────────┼─────────────┼───────────────┨\n" +
"┃ sr.apply(…) │ 3 │ sum 3 │ s 3 ┃\n" +
"┃ sr.apply(…) │ 5 │ sum 5 │ s 5 ┃\n" +
"┃ sr.agg(…) │ │ │ ┃\n" + "┃ sr.agg(…) │ │ │ ┃\n" +
"┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛\n" + "┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛\n" +
"\n" + "\n" +
@ -816,6 +824,7 @@ function fixHighlights() {
$(`code:contains(>>> logging.basicConfig(level=)`).html(LOGGING_EXAMPLE); $(`code:contains(>>> logging.basicConfig(level=)`).html(LOGGING_EXAMPLE);
$(`code:contains(samples_f = (sin(i *)`).html(AUDIO); $(`code:contains(samples_f = (sin(i *)`).html(AUDIO);
$(`code:contains(collections, dataclasses, enum, io, itertools)`).html(MARIO); $(`code:contains(collections, dataclasses, enum, io, itertools)`).html(MARIO);
$(`code:contains(>>> gb = df.groupby)`).html(PLOTLY);
$(`ul:contains(Only available in)`).html(INDEX); $(`ul:contains(Only available in)`).html(INDEX);
} }

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