@@ -77,9 +77,9 @@ I want to work with the dates in the column ``datetime`` as datetime objects ins
77
77
78
78
Initially, the values in ``datetime `` are character strings and do not
79
79
provide any datetime operations (e.g. extract the year, day of the
80
- week,…). By applying the ``to_datetime `` function, pandas interprets the
80
+ week, …). By applying the ``to_datetime `` function, pandas interprets the
81
81
strings and convert these to datetime (i.e. ``datetime64[ns, UTC] ``)
82
- objects. In pandas we call these datetime objects similar to
82
+ objects. In pandas we call these datetime objects that are similar to
83
83
``datetime.datetime `` from the standard library as :class: `pandas.Timestamp `.
84
84
85
85
.. raw :: html
@@ -117,7 +117,7 @@ length of our time series:
117
117
air_quality[" datetime" ].max() - air_quality[" datetime" ].min()
118
118
119
119
The result is a :class: `pandas.Timedelta ` object, similar to ``datetime.timedelta ``
120
- from the standard Python library and defining a time duration.
120
+ from the standard Python library which defines a time duration.
121
121
122
122
.. raw :: html
123
123
@@ -257,7 +257,7 @@ the adapted time scale on plots. Let’s apply this on our data.
257
257
<ul class =" task-bullet" >
258
258
<li >
259
259
260
- Create a plot of the :math: `NO_2 ` values in the different stations from the 20th of May till the end of 21st of May
260
+ Create a plot of the :math: `NO_2 ` values in the different stations from May 20th till the end of May 21st.
261
261
262
262
.. ipython :: python
263
263
:okwarning:
@@ -310,7 +310,7 @@ converting secondly data into 5-minutely data).
310
310
The :meth: `~Series.resample ` method is similar to a groupby operation:
311
311
312
312
- it provides a time-based grouping, by using a string (e.g. ``M ``,
313
- ``5H ``,…) that defines the target frequency
313
+ ``5H ``, …) that defines the target frequency
314
314
- it requires an aggregation function such as ``mean ``, ``max ``,…
315
315
316
316
.. raw :: html
0 commit comments