* **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 'dayfirst' 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/…'`.**
### GroupBy
**Object that groups together rows of a dataframe based on the value of the passed column.**
<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 'dayfirst' 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>
<div><h3id="groupby">GroupBy</h3><p><strong>Object that groups together rows of a dataframe based on the value of the passed column.</strong></p><pre><codeclass="python language-python hljs"><GB> = <DF>.groupby(col_key/s) <spanclass="hljs-comment"># Splits DF into groups based on passed column.</span>
<DF> = <GB>.apply/filter(<func>) <spanclass="hljs-comment"># Filter drops a group if func returns False.</span>
Jure Šorn
1 month ago