Skip to content

Course Notes

Use this workspace to take notes, store code snippets, or build your own interactive cheatsheet! The datasets used in this course are available in the datasets folder.

# Import any packages you want to use here

Take Notes

Add notes here about the concepts you've learned and code cells with code you want to keep.

Merging and concatenating:

Add your notes here



artists.merge(albums, on='artid').head()
# Concatenate the classic tables vertically
classic_18_19 = pd.concat([classic_18,classic_19], ignore_index=True)

# Using .isin(), filter classic_18_19 rows where tid is in classic_pop
popular_classic = classic_18_19[classic_18_19["tid"].isin(classic_pop["tid"])]

# Use merge_ordered() to merge gdp and sp500 on year and date
gdp_sp500 = pd.merge_ordered(gdp, sp500, left_on="year", right_on="date", how="left")

# Use merge_ordered() to merge gdp and sp500, interpolate missing value
gdp_sp500 = pd.merge_ordered(gdp, sp500, left_on="year", right_on="date", how="left", fill_method="ffill")

# Use merge_ordered() to merge gdp and sp500, interpolate missing value
gdp_sp500 = pd.merge_ordered(gdp, sp500, left_on='year', right_on='date', 
                             how='left',  fill_method='ffill')

# Subset the gdp and returns columns
gdp_returns = gdp_sp500[["gdp","returns"]]

# Print gdp_returns correlation
print (gdp_returns.corr())