diff --git a/README.md b/README.md index b66227b..dc4745f 100644 --- a/README.md +++ b/README.md @@ -3187,7 +3187,8 @@ Name: a, dtype: int64 ```python = .head/describe/sort_values() # Also .unique/value_counts/round/dropna(). = .str.strip/lower/contains/replace() # Also split().str[i] or split(expand=True). - = .dt.year/month/day/hour # Use pd.to_datetime() to get S of dates. + = .dt.year/month/day/hour # Use pd.to_datetime() to get S of datetimes. + = .dt.to_period('y/m/d/h') # Quantizes datetimes into Period objects. ``` ```python @@ -3223,7 +3224,6 @@ Name: a, dtype: int64 | | y 2.0 | y 2.0 | y 2.0 | +--------------+-------------+-------------+---------------+ ``` -* **Agg() and transform() pass a Series to a function if it raises Type/Val/AttrError on a scalar.** * **Last result has a multi-index. Use `'[key_1, key_2]'` to get its values.** ### DataFrame @@ -3366,7 +3366,7 @@ c 6 7 ``` * **`'$ pip3 install "pandas[excel]" odfpy lxml pyarrow'` installs dependencies.** * **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 `' = pd.to_datetime(, errors="coerce")'`, which uses pd.NaT.** +* **If 'parse\_dates' and 'index_col' are the same column, we get a DF with DatetimeIndex. Its `'resample("y/m/d/h")'` method returns a Resampler object that is similar to GroupBy.** ### GroupBy **Object that groups together rows of a dataframe based on the value of the passed column.** diff --git a/index.html b/index.html index bb366d1..252a61b 100644 --- a/index.html +++ b/index.html @@ -56,7 +56,7 @@
- +
@@ -2607,7 +2607,8 @@ Name: a, dtype: int64 
<S>  = <S>.head/describe/sort_values()         # Also <S>.unique/value_counts/round/dropna().
 <S>  = <S>.str.strip/lower/contains/replace()  # Also split().str[i] or split(expand=True).
-<S>  = <S>.dt.year/month/day/hour              # Use pd.to_datetime(<S>) to get S of dates.
+<S>  = <S>.dt.year/month/day/hour              # Use pd.to_datetime(<S>) to get S of datetimes.
+<S>  = <S>.dt.to_period('y/m/d/h')             # Quantizes datetimes into Period objects.
 
<S>.plot.line/area/bar/pie/hist()              # Generates a plot. `plt.show()` displays it.
@@ -2639,7 +2640,6 @@ Name: a, dtype: int64
    -
  • Agg() and transform() pass a Series to a function if it raises Type/Val/AttrError on a scalar.
  • Last result has a multi-index. Use '<S>[key_1, key_2]' to get its values.

DataFrame

Table with labeled rows and columns.

>>> df = pd.DataFrame([[1, 2], [3, 4]], index=['a', 'b'], columns=['x', 'y']); df
@@ -2754,7 +2754,7 @@ c  6  7
 
    
 
  • '$ pip3 install "pandas[excel]" odfpy lxml pyarrow' installs dependencies.
    • 
 
  • 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.
    • 
+
  • If 'parse_dates' and 'index_col' are the same column, we get a DF with DatetimeIndex. Its 'resample("y/m/d/h")' method returns a Resampler object that is similar to GroupBy.
    • 
 

 
GroupBy
Object that groups together rows of a dataframe based on the value of the passed column.
<GB> = <DF>.groupby(col_key/s)                 # Splits DF into groups based on passed column.
 <DF> = <GB>.apply/filter(<func>)               # Filter drops a group if func returns False.
@@ -2942,7 +2942,7 @@ $ deactivate                # Deactivates the active