<S> = pd.Series(<list>) # Creates index from list's indices.
<S> = pd.Series(<dict>) # Creates index from dictionary's keys.
<S> = pd.Series(<list>) # Uses list's indices for 'index'.
<S> = pd.Series(<dict>) # Uses dictionary's keys for 'index'.
```
```python
@ -3365,7 +3365,7 @@ c 6 7
<DF>.to_pickle/excel(<path>) # Run `$ pip3 install "pandas[excel]" odfpy`.
<DF>.to_sql('<table_name>', <connection>) # Also `if_exists='fail/replace/append'`.
```
* **Read\_csv() only parses dates of columns that were specified by 'parse\_dates' argument. Itautomatically tries to detect the format, but it can be helped with 'date\_format' or 'datefirst' arguments. Both dates and datetimes get stored as pd.Timestamp objects.**
* **Read\_csv() only parses dates of columns that were specified by 'parse\_dates' argument. Itautomatically tries to detect the format, but it can be helped with 'date\_format' or 'datefirst' arguments. Both dates and datetimes get stored as pd.Timestamp objects.**
* **If there's a single invalid date then it returns the whole column as a series of strings, unlike `'<S> = pd.to_datetime(<S>, errors="coerce")'`, which uses pd.NaT.**
* **To get specific attributes from a series of Timestamps use `'<S>.dt.year/date/…'`.**
@ -2741,7 +2741,7 @@ c <span class="hljs-number">6</span> <span class="hljs-number">7</span>
<DF>.to_sql(<spanclass="hljs-string">'<table_name>'</span>, <connection>) <spanclass="hljs-comment"># Also `if_exists='fail/replace/append'`.</span>
</code></pre>
<ul>
<li><strong>Read_csv() only parses dates of columns that were specified by 'parse_dates' argument. It automatically tries to detect the format, but it can be helped with 'date_format' or 'datefirst' arguments. Both dates and datetimes get stored as pd.Timestamp objects.</strong></li>
<li><strong>Read_csv() only parses dates of columns that were specified by 'parse_dates' argument. It automatically tries to detect the format, but it can be helped with 'date_format' or 'datefirst' arguments. Both dates and datetimes get stored as pd.Timestamp objects.</strong></li>
<li><strong>If there's a single invalid date then it returns the whole column as a series of strings, unlike <codeclass="python hljs"><spanclass="hljs-string">'<S> = pd.to_datetime(<S>, errors="coerce")'</span></code>, which uses pd.NaT.</strong></li>
<li><strong>To get specific attributes from a series of Timestamps use <codeclass="python hljs"><spanclass="hljs-string">'<S>.dt.year/date/…'</span></code>.</strong></li>
</ul>
@ -2924,7 +2924,7 @@ $ deactivate <span class="hljs-comment"># Deactivates the active
Jure Šorn
2 months ago