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python-cheatsheet
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Pandas

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Jure Šorn 2 months ago
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  1. 10
      README.md
  2. 10
      index.html

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README.md
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@ -3201,7 +3201,7 @@ Name: a, dtype: int64
#### Series — Aggregate, Transform, Map:
```python
<el> = <S>.sum/max/mean/idxmax/all/count() # Or: <S>.agg(lambda <S>: <el>)
<el> = <S>.sum/max/mean/std/idxmax/count() # Or: <S>.agg(lambda <S>: <el>)
<S> = <S>.rank/diff/cumsum/ffill/interpol…() # Or: <S>.agg/transform(lambda <S>: <S>)
<S> = <S>.isna/fillna/isin([<el/coll>]) # Or: <S>.agg/transform/map(lambda <el>: <el>)
```
@ -3314,7 +3314,7 @@ c 6 7
#### DataFrame — Aggregate, Transform, Map:
```python
<S> = <DF>.sum/max/mean/idxmax/all/count() # Or: <DF>.apply/agg(lambda <S>: <el>)
<S> = <DF>.sum/max/mean/std/idxmax/count() # Or: <DF>.apply/agg(lambda <S>: <el>)
<DF> = <DF>.rank/diff/cumsum/ffill/interpo…() # Or: <DF>.apply/agg/transform(lambda <S>: <S>)
<DF> = <DF>.isna/fillna/isin([<el/coll>]) # Or: <DF>.applymap(lambda <el>: <el>)
```
@ -3355,13 +3355,13 @@ c 6 7
<S/DF> = pd.read_json/pickle(<path/url/file>) # Also io.StringIO(<str>), io.BytesIO(<bytes>).
<DF> = pd.read_csv/excel(<path/url/file>) # Also `header/index_col/dtype/usecols/…=<obj>`.
<list> = pd.read_html(<path/url/file>) # Raises ImportError if webpage has zero tables.
<S/DF> = pd.read_parquet/feather/hdf(<path…>) # Read_hdf() accepts `key=<s/df_name>` argument.
<S/DF> = pd.read_parquet/feather/hdf(<path…>) # Function read_hdf() accepts `key=<s/df_name>`.
<DF> = pd.read_sql('<table/query>', <conn>) # Pass SQLite3/Alchemy connection (see #SQLite).
```
```python
<DF>.to_json/csv/html/parquet/latex(<path>) # Returns a string/bytes if path is omitted.
<DF>.to_pickle/excel/feather/hdf(<path>) # To_hdf() requires `key=<s/df_name>` argument.
<DF>.to_pickle/excel/feather/hdf(<path>) # Method to_hdf() requires `key=<s/df_name>`.
<DF>.to_sql('<table_name>', <connection>) # Also `if_exists='fail/replace/append'`.
```
* **`'$ pip3 install "pandas[excel]" odfpy lxml pyarrow'` installs dependencies.**
@ -3380,7 +3380,7 @@ c 6 7
```
```python
<DF> = <GB>.sum/max/mean/idxmax/all() # Or: <GB>.agg(lambda <S>: <el>)
<DF> = <GB>.sum/max/mean/std/idxmax/count() # Or: <GB>.agg(lambda <S>: <el>)
<DF> = <GB>.rank/diff/cumsum/ffill() # Or: <GB>.transform(lambda <S>: <S>)
<DF> = <GB>.fillna(<el>) # Or: <GB>.transform(lambda <S>: <S>)
```

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@ -2618,7 +2618,7 @@ Name: a, dtype: int64
<li><strong>Pandas uses NumPy types like <code class="python hljs"><span class="hljs-string">'np.int64'</span></code>. Series is converted to <code class="python hljs"><span class="hljs-string">'float64'</span></code> if we assign np.nan to any item. Use <code class="python hljs"><span class="hljs-string">'&lt;S&gt;.astype(&lt;str/type&gt;)'</span></code> to get converted Series.</strong></li>
<li><strong>Series will silently overflow if we run <code class="python hljs"><span class="hljs-string">'pd.Series([100], dtype="int8") + 100'</span></code>!</strong></li>
</ul>
<div><h4 id="seriesaggregatetransformmap">Series — Aggregate, Transform, Map:</h4><pre><code class="python language-python hljs">&lt;el&gt; = &lt;S&gt;.sum/max/mean/idxmax/all/count() <span class="hljs-comment"># Or: &lt;S&gt;.agg(lambda &lt;S&gt;: &lt;el&gt;)</span>
<div><h4 id="seriesaggregatetransformmap">Series — Aggregate, Transform, Map:</h4><pre><code class="python language-python hljs">&lt;el&gt; = &lt;S&gt;.sum/max/mean/std/idxmax/count() <span class="hljs-comment"># Or: &lt;S&gt;.agg(lambda &lt;S&gt;: &lt;el&gt;)</span>
&lt;S&gt; = &lt;S&gt;.rank/diff/cumsum/ffill/interpol…() <span class="hljs-comment"># Or: &lt;S&gt;.agg/transform(lambda &lt;S&gt;: &lt;S&gt;)</span>
&lt;S&gt; = &lt;S&gt;.isna/fillna/isin([&lt;el/coll&gt;]) <span class="hljs-comment"># Or: &lt;S&gt;.agg/transform/map(lambda &lt;el&gt;: &lt;el&gt;)</span>
</code></pre></div>
@ -2707,7 +2707,7 @@ c <span class="hljs-number">6</span> <span class="hljs-number">7</span>
┃ │ c . . <span class="hljs-number">6</span> <span class="hljs-number">7</span> │ │ │ treated as a column. ┃
┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
</code></pre>
<div><h4 id="dataframeaggregatetransformmap">DataFrame — Aggregate, Transform, Map:</h4><pre><code class="python language-python hljs">&lt;S&gt; = &lt;DF&gt;.sum/max/mean/idxmax/all/count() <span class="hljs-comment"># Or: &lt;DF&gt;.apply/agg(lambda &lt;S&gt;: &lt;el&gt;)</span>
<div><h4 id="dataframeaggregatetransformmap">DataFrame — Aggregate, Transform, Map:</h4><pre><code class="python language-python hljs">&lt;S&gt; = &lt;DF&gt;.sum/max/mean/std/idxmax/count() <span class="hljs-comment"># Or: &lt;DF&gt;.apply/agg(lambda &lt;S&gt;: &lt;el&gt;)</span>
&lt;DF&gt; = &lt;DF&gt;.rank/diff/cumsum/ffill/interpo…() <span class="hljs-comment"># Or: &lt;DF&gt;.apply/agg/transform(lambda &lt;S&gt;: &lt;S&gt;)</span>
&lt;DF&gt; = &lt;DF&gt;.isna/fillna/isin([&lt;el/coll&gt;]) <span class="hljs-comment"># Or: &lt;DF&gt;.applymap(lambda &lt;el&gt;: &lt;el&gt;)</span>
</code></pre></div>
@ -2743,12 +2743,12 @@ c <span class="hljs-number">6</span> <span class="hljs-number">7</span>
<div><h3 id="fileformats">File Formats</h3><pre><code class="python language-python hljs">&lt;S/DF&gt; = pd.read_json/pickle(&lt;path/url/file&gt;) <span class="hljs-comment"># Also io.StringIO(&lt;str&gt;), io.BytesIO(&lt;bytes&gt;).</span>
&lt;DF&gt; = pd.read_csv/excel(&lt;path/url/file&gt;) <span class="hljs-comment"># Also `header/index_col/dtype/usecols/…=&lt;obj&gt;`.</span>
&lt;list&gt; = pd.read_html(&lt;path/url/file&gt;) <span class="hljs-comment"># Raises ImportError if webpage has zero tables.</span>
&lt;S/DF&gt; = pd.read_parquet/feather/hdf(&lt;path…&gt;) <span class="hljs-comment"># Read_hdf() accepts `key=&lt;s/df_name&gt;` argument.</span>
&lt;S/DF&gt; = pd.read_parquet/feather/hdf(&lt;path…&gt;) <span class="hljs-comment"># Function read_hdf() accepts `key=&lt;s/df_name&gt;`.</span>
&lt;DF&gt; = pd.read_sql(<span class="hljs-string">'&lt;table/query&gt;'</span>, &lt;conn&gt;) <span class="hljs-comment"># Pass SQLite3/Alchemy connection (see #SQLite).</span>
</code></pre></div>
<pre><code class="python language-python hljs">&lt;DF&gt;.to_json/csv/html/parquet/latex(&lt;path&gt;) <span class="hljs-comment"># Returns a string/bytes if path is omitted.</span>
&lt;DF&gt;.to_pickle/excel/feather/hdf(&lt;path&gt;) <span class="hljs-comment"># To_hdf() requires `key=&lt;s/df_name&gt;` argument.</span>
&lt;DF&gt;.to_pickle/excel/feather/hdf(&lt;path&gt;) <span class="hljs-comment"># Method to_hdf() requires `key=&lt;s/df_name&gt;`.</span>
&lt;DF&gt;.to_sql(<span class="hljs-string">'&lt;table_name&gt;'</span>, &lt;connection&gt;) <span class="hljs-comment"># Also `if_exists='fail/replace/append'`.</span>
</code></pre>
<ul>
@ -2764,7 +2764,7 @@ c <span class="hljs-number">6</span> <span class="hljs-number">7</span>
</code></pre></div>
<pre><code class="python language-python hljs">&lt;DF&gt; = &lt;GB&gt;.sum/max/mean/idxmax/all() <span class="hljs-comment"># Or: &lt;GB&gt;.agg(lambda &lt;S&gt;: &lt;el&gt;)</span>
<pre><code class="python language-python hljs">&lt;DF&gt; = &lt;GB&gt;.sum/max/mean/std/idxmax/count() <span class="hljs-comment"># Or: &lt;GB&gt;.agg(lambda &lt;S&gt;: &lt;el&gt;)</span>
&lt;DF&gt; = &lt;GB&gt;.rank/diff/cumsum/ffill() <span class="hljs-comment"># Or: &lt;GB&gt;.transform(lambda &lt;S&gt;: &lt;S&gt;)</span>
&lt;DF&gt; = &lt;GB&gt;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;GB&gt;.transform(lambda &lt;S&gt;: &lt;S&gt;)</span>
</code></pre>

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