diff --git a/README.md b/README.md index dfe53c3..e83499f 100644 --- a/README.md +++ b/README.md @@ -3164,8 +3164,8 @@ Name: a, dtype: int64 ``` ```python - = pd.Series() # Creates index from list's indices. - = pd.Series() # Creates index from dictionary's keys. + = pd.Series() # Uses list's indices for 'index'. + = pd.Series() # Uses dictionary's keys for 'index'. ``` ```python @@ -3365,7 +3365,7 @@ c 6 7 .to_pickle/excel() # Run `$ pip3 install "pandas[excel]" odfpy`. .to_sql('', ) # Also `if_exists='fail/replace/append'`. ``` -* **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.** +* **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.** * **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.** * **To get specific attributes from a series of Timestamps use `'.dt.year/date/…'`.** diff --git a/index.html b/index.html index d18dab7..68761a2 100644 --- a/index.html +++ b/index.html @@ -55,7 +55,7 @@
- +
@@ -2580,8 +2580,8 @@ Name: a, dtype: int64 -
<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'.
 

 
<el> = <S>.loc[key]                            # Or: <S>.iloc[i]
 <S>  = <S>.loc[coll_of_keys]                   # Or: <S>.iloc[coll_of_i]
@@ -2741,7 +2741,7 @@ c  6  7
 <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. 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.
    • 
+
  • 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.
    • 
 
  • 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/…'.
    • 
 

@@ -2924,7 +2924,7 @@ $ deactivate                # Deactivates the active