Merge pull request #97 from chennesy/jt14den-patch-2

removing date from data-visualisation.md

Author: chennesy

episodes/data-visualisation.md

@@ -33,86 +33,6 @@ Let’s look at the data:
 df_long.head()
 ```
 
-|     | branch         | address                 | city    | zip code | ytd    | year | month   | circulation |
-|-----|----------------|-------------------------|---------|----------|--------|------|---------|-------------|
-| 0   | Albany Park    | 5150 N. Kimball Ave.    | Chicago | 60625.0  | 120059 | 2011 | january | 8427        |
-| 1   | Altgeld        | 13281 S. Corliss Ave.   | Chicago | 60827.0  | 9611   | 2011 | january | 1258        |
-| 2   | Archer Heights | 5055 S. Archer Ave.     | Chicago | 60632.0  | 101951 | 2011 | january | 8104        |
-| 3   | Austin         | 5615 W. Race Ave.       | Chicago | 60644.0  | 25527  | 2011 | january | 1755        |
-| 4   | Austin-Irving  | 6100 W. Irving Park Rd. | Chicago | 60634.0  | 165634 | 2011 | january | 12593       |
-
-## Convert year and month to datetime
-
-In order to plot this data over time we need to do two things to prepare it first. First, we need to combine the year and month columns into a single [datetime](https://docs.python.org/3/library/datetime.html) column using the Pandas `to_datetime` function. Second, we assign the date column as our index for the data. These two steps will set up our data for plotting.
-
-``` python
-df_long['date'] = pd.to_datetime(df_long['year'].astype(str) + '-' + df_long['month'], format='%Y-%B')
-```
-
-Let's unpack that code:
-
-- `df_long['date']` - First, we create a new `date` column. 
-- `pd.to_datetime()` - Next we package everything into a datetime object.
-- `df_long['year'].astype(str)` - We use the `.astype(str)` method to convert the year column to a string
-- `+ '-' + df_long['month'],` - We concatenate a `-` to the string as a separator, followed by the month column.
-- `format='%Y-%B'` - We pass the datetime parameter to tell Python to expect a 4 digit year (%Y), followed by a dash, followed by the month's full name (%B).
-
-If we take a look at the date column, we'll see that datetime automatically adds a day (always `01`) in the absence of any specific day input.
-
-```python
-df_long['date']
-```
-```output
-0       2011-01-01
-1       2011-01-01
-2       2011-01-01
-3       2011-01-01
-4       2011-01-01
-           ...    
-11551   2022-12-01
-11552   2022-12-01
-11553   2022-12-01
-11554   2022-12-01
-11555   2022-12-01
-Name: date, Length: 11556, dtype: datetime64[ns]
-```
-
-``` python
-df_long.info()
-```
-
-``` output
-<class 'pandas.core.frame.DataFrame'>
-RangeIndex: 11556 entries, 0 to 11555
-Data columns (total 9 columns):
- #   Column       Non-Null Count  Dtype         
----  ------       --------------  -----         
- 0   branch       11556 non-null  object        
- 1   address      7716 non-null   object        
- 2   city         7716 non-null   object        
- 3   zip code     7716 non-null   float64       
- 4   ytd          11556 non-null  int64         
- 5   year         11556 non-null  object        
- 6   month        11556 non-null  object        
- 7   circulation  11556 non-null  int64         
- 8   date         11556 non-null  datetime64[ns]
-dtypes: datetime64[ns](1), float64(1), int64(2), object(5)
-memory usage: 812.7+ KB
-```
-
-That worked! Now, we can make the datetime column the index of our DataFrame. In the Pandas episode we looked at Pandas default numerical index, but we can also use `.set_index()` to declare a specific column as the index of our DataFrame. Using a datetime index will make it easier for us to plot the DataFrame over time. The first parameter of `.set_index()` is the column name and the `inplace=True` parameter allows us to modify the DataFrame without assigning it to a new variable.
-
-
-``` python
-df_long.set_index('date', inplace=True)
-```
-
-If we look at the data again, we will see our index will be set to date.
-
-``` python
-df_long.head()
-```
-
 |            | branch         | address                 | city    | zip code | ytd    | year | month   | circulation |
 |------------|----------------|-------------------------|---------|----------|--------|------|---------|-------------|
 | date       |                |                         |         |          |        |      |         |             |