FinTech 03-3 Pandas

Which were objectives of Unit 03 Day 3 Python-Pandas?

• Grouping data in DataFrames

• Plotting with matplotlib

• Working with datetime data

• Applying mean, median, and standard deviation

• Describe and calculate Sharpe ratios

Which Pandas functions did we use?

• concat()

• sort_values()

• random()

• groupby()

• std()

What must you do with a groupby object to display results?

• Call an aggregating function on it

• Specify columns to sort

• Sort the result

• Plot it

Which are aggregating functions?

• .count()

• .divide()

• .sum()

• .mean()

• .max()

Which steps could you perform to prepare a stock price data set for analysis?

• Convert any time data into datetime type

• Drop or fill null values

• Group by company CEO

• Remove non-numeric characters like $

• Set a multi-index to group dates and tickers

What do you need to know before joining two data sets?

• If you're joining columns or rows

• The names of the files or DataFrames

• Which records you want to keep if they don't match

• How many rows each data set has

Which is the Pandas syntax to create df3 by concatenating df1 and df2 as additional rows?

• df3 = pd.concat(df1, df2)

• df3 = concat(df1, df2, join = 'outer')

• df3 = pd.concat(df1, df2, axis = 1)

• df3 = concat(df1, df2, axis = 'rows')

Calculate the standard deviation for all columns in DataFrame all_returns:

How was Unit 03-Day 3 for you?

Any suggestions for improvement?