How to iterate over columns of pandas dataframe to run regression

Question

I have this code using Pandas in Python:

all_data = {}
for ticker in ['FIUIX', 'FSAIX', 'FSAVX', 'FSTMX']:
    all_data[ticker] = web.get_data_yahoo(ticker, '1/1/2010', '1/1/2015')

prices = DataFrame({tic: data['Adj Close'] for tic, data in all_data.iteritems()})  
returns = prices.pct_change()

I know I can run a regression like this:

regs = sm.OLS(returns.FIUIX,returns.FSTMX).fit()

but how can I do this for each column in the dataframe? Specifically, how can I iterate over columns, in order to run the regression on each?

Specifically, I want to regress each other ticker symbol (FIUIX, FSAIX and FSAVX) on FSTMX, and store the residuals for each regression.

I've tried various versions of the following, but nothing I've tried gives the desired result:

resids = {}
for k in returns.keys():
    reg = sm.OLS(returns[k],returns.FSTMX).fit()
    resids[k] = reg.resid

Is there something wrong with the returns[k] part of the code? How can I use the k value to access a column? Or else is there a simpler approach?

Answer 1

for column in df:
    print(df[column])

Answer 2

You can use iteritems():

for name, values in df.iteritems():
    print('{name}: {value}'.format(name=name, value=values[0]))

Answer 3

This answer is to iterate over selected columns as well as all columns in a DF.

df.columns gives a list containing all the columns' names in the DF. Now that isn't very helpful if you want to iterate over all the columns. But it comes in handy when you want to iterate over columns of your choosing only.

We can use Python's list slicing easily to slice df.columns according to our needs. For eg, to iterate over all columns but the first one, we can do:

for column in df.columns[1:]:
    print(df[column])

Similarly to iterate over all the columns in reversed order, we can do:

for column in df.columns[::-1]:
    print(df[column])

We can iterate over all the columns in a lot of cool ways using this technique. Also remember that you can get the indices of all columns easily using:

for ind, column in enumerate(df.columns):
    print(ind, column)

Answer 4

You can index dataframe columns by the position using ix.

df1.ix[:,1]

This returns the first column for example. (0 would be the index)

df1.ix[0,]

This returns the first row.

df1.ix[:,1]

This would be the value at the intersection of row 0 and column 1:

df1.ix[0,1]

and so on. So you can enumerate() returns.keys(): and use the number to index the dataframe.

Answer 5

A workaround is to transpose the DataFrame and iterate over the rows.

for column_name, column in df.transpose().iterrows():
    print column_name

Answer 6

Using list comprehension, you can get all the columns names (header):

[column for column in df]

Answer 7

Based on the accepted answer, if an index corresponding to each column is also desired:

for i, column in enumerate(df):
    print i, df[column]

The above df[column] type is Series, which can simply be converted into numpy ndarrays:

for i, column in enumerate(df):
    print i, np.asarray(df[column])

Answer 8

I'm a bit late but here's how I did this. The steps:

1. Create a list of all columns
2. Use itertools to take x combinations
3. Append each result R squared value to a result dataframe along with excluded column list
4. Sort the result DF in descending order of R squared to see which is the best fit.

This is the code I used on DataFrame called aft_tmt. Feel free to extrapolate to your use case..

import pandas as pd
# setting options to print without truncating output
pd.set_option('display.max_columns', None)
pd.set_option('display.max_colwidth', None)

import statsmodels.formula.api as smf
import itertools

# This section gets the column names of the DF and removes some columns which I don't want to use as predictors.
itercols = aft_tmt.columns.tolist()
itercols.remove("sc97")
itercols.remove("sc")
itercols.remove("grc")
itercols.remove("grc97")
print itercols
len(itercols)

# results DF
regression_res = pd.DataFrame(columns = ["Rsq", "predictors", "excluded"])

# excluded cols
exc = []

# change 9 to the number of columns you want to combine from N columns.
#Possibly run an outer loop from 0 to N/2?
for x in itertools.combinations(itercols, 9):
    lmstr = "+".join(x)
    m = smf.ols(formula = "sc ~ " + lmstr, data = aft_tmt)
    f = m.fit()
    exc = [item for item in x if item not in itercols]
    regression_res = regression_res.append(pd.DataFrame([[f.rsquared, lmstr, "+".join([y for y in itercols if y not in list(x)])]], columns = ["Rsq", "predictors", "excluded"]))

regression_res.sort_values(by="Rsq", ascending = False)

Answer 9

I landed on this question as I was looking for a clean iterator of columns only (Series, no names).

Unless I am mistaken, there is no such thing, which, if true, is a bit annoying. In particular, one would sometimes like to assign a few individual columns (Series) to variables, e.g.:

x, y = df[['x', 'y']]  # does not work

There is df.items() that gets close, but it gives an iterator of tuples (column_name, column_series). Interestingly, there is a corresponding df.keys() which returns df.columns, i.e. the column names as an Index, so a, b = df[['x', 'y']].keys() assigns properly a='x' and b='y'. But there is no corresponding df.values(), and for good reason, as df.values is a property and returns the underlying numpy array.

One (inelegant) way is to do:

x, y = (v for _, v in df[['x', 'y']].items())

but it's less pythonic than I'd like.

Answer 10

Most of these answers are going via the column name, rather than iterating the columns directly. They will also have issues if there are multiple columns with the same name. If you want to iterate the columns, I'd suggest:

for series in (df.iloc[:,i] for i in range(df.shape[1])):
   ...

Answer 11

assuming X-factor, y-label (multicolumn):

columns = [c for c in _df.columns if c in ['col1', 'col2','col3']]  #or '..c not in..'
_df.set_index(columns, inplace=True)
print( _df.index)

X, y =  _df.iloc[:,:4].values, _df.index.values