Update timeseries.md

lectures/pandas/timeseries.md

@@ -36,12 +36,13 @@ kernelspec:
 
 ```{code-cell} python
 import os
+# see section on API keys at end of lecture!
+os.environ["NASDAQ_DATA_LINK_API_KEY"] = "jEKP58z7JaX6utPkkpEp"
+
 import pandas as pd
 import matplotlib.pyplot as plt
 import nasdaqdatalink as ndl
 
-# see section on API keys at end of lecture!
-os.environ["NASDAQ_DATA_LINK_API_KEY"] = "jEKP58z7JaX6utPkkpEp"
 start_date = "2014-05-01"
 
 %matplotlib inline
@@ -197,17 +198,28 @@ The flexibility of these features is best understood through example,
 so let's load up some data and take a look.
 
 ```{code-cell} python
-btc_usd = ndl.get_table("QDL/BCHAIN")
+btc_usd = ndl.get_table("QDL/BCHAIN", paginate=True)
 btc_usd.info()
 btc_usd.head()
 ```
 
 Here, we have the Bitcoin (BTC) to US dollar (USD) exchange rate from
-March 2014 until today.
+2009 until today, as well as other variables relevant to the Bitcoin ecosystem, in long ("melted") form.
+
+```{code-cell} python
+print(btc_usd.code.unique())
+btc_usd.dtypes
+```
+
+Notice that the type of `date` is `datetime`. We would like this to be the index, and we want to drop the long form. We'll also select only a couple of columns of interest. (The column descriptions can be found [here](https://data.nasdaq.com/databases/BCHAIN)). We'll choose Market Price (in USD) (`MKPRU`), Total Market Cap (`MKTCP`), and Estimated Transaction Volume in USD (`ETRVU`).
 
-Notice that the type of index is `DateTimeIndex`.
+```{code-cell} python
+btc_usd = btc_usd.pivot_table(index='date', columns='code', values='value')
+btc_usd = btc_usd[["MKPRU", "MKTCP", "ETRVU"]]
+btc_usd.head()
+```
 
-This is the key that enables things like...
+Now that we have a datetime index, it enables things like...
 
 Extracting all data for the year 2015 by passing `"2015"` to `.loc`.
 
@@ -289,11 +301,11 @@ btc_date_column.head()
 ```
 
 ```{code-cell} python
-btc_date_column["Date"].dt.year.head()
+btc_date_column["date"].dt.year.head()
 ```
 
 ```{code-cell} python
-btc_date_column["Date"].dt.month.head()
+btc_date_column["date"].dt.month.head()
 ```
 
 ## Leads and Lags: `df.shift`
@@ -379,8 +391,8 @@ window for the whole dataset.
 
 ```{code-cell} python
 fig, ax = plt.subplots(figsize=(10, 4))
-btc_usd["Open"].plot(ax=ax, linestyle="--", alpha=0.8)
-btc_usd.rolling("21d").max()["Open"].plot(ax=ax, alpha=0.8, linewidth=3)
+btc_usd["MKPRU"].plot(ax=ax, linestyle="--", alpha=0.8)
+btc_usd.rolling("21d").max()["MKPRU"].plot(ax=ax, alpha=0.8, linewidth=3)
 ax.legend(["Original", "21 day max"])
 ```