Python中的DataFrame的DataFrame（Pandas）

Question

The idea here is that for every year, I am able to create three dataframes(df1, df2, df3), each containing different firms and stock prices('firm' and 'price' are the two columns in df1~df3). I would like to use another dataframe (named 'store' below) to store the three dataframes every year.

Here is what I code:

store = pd.DataFrame(list(range(1967,2014)), columns=['year'])
for year in range(1967,2014):
    ....some codes that allow me to generate df1, df2 and df3 correctly...
    store.loc[store['year']==year, 'df1']=df1
    store.loc[store['year']==year, 'df2']=df2
    store.loc[store['year']==year, 'df3']=df3

I am not getting error warning or anything after this code. But in the "store" dataframe, columns 'df1', 'df2' and 'df3' are all 'NAN' values.

Answer 1

I think that pandas offers better alternatives to what you're suggesting (rationale below).

For one, there's the pandas.Panel data structure, which was meant for things like you're doing here.

However, as Wes McKinney (the Pandas author) noted in his book Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython , multi-dimensional indices, to a large extent, offer a better alternative.

Consider the following alternative to your code:

dfs = []
for year in range(1967,2014):
    ....some codes that allow me to generate df1, df2 and df3 
    df1['year'] = year
    df1['origin'] = 'df1'
    df2['year'] = year
    df2['origin'] = 'df2'
    df3['year'] = year
    df3['origin'] = 'df3'
    dfs.extend([df1, df2, df3])
df = pd.concat(dfs)

This gives you a DataFrame with 4 columns: 'firm' , 'price' , 'year' , and 'origin' .

This gives you the flexibility to:

• Organize hierarchically by, say, 'year' and 'origin' : df.set_index(['year', 'origin']) , by, say, 'origin' and 'price' : df.set_index(['origin', 'price'])

• Do groupby s according to different levels

• In general, slice and dice the data along many different ways.

What you're suggesting in the question makes one dimension (origin) arbitrarily different, and it's hard to think of an advantage to this. If a split along some dimension is necessary due, to, eg, performance, you can combine DataFrames better with standard Python data structures:

• A dictionary mapping each year to a Dataframe with the other three dimensions.

• Three DataFrames, one for each origin, each having three dimensions.