Go from wide format panel data to long format. Similar to Stata's reshape.

wide_to_long.py

def wide_to_long(df, stubnames, i, j):
    """
    User-friendly wide panel to long format.

    Parameters
    ----------
    df : DataFrame
        The wide-format DataFrame
    stubnames : list
        A list of stub names. The wide format variables are assumed to
        start with the stub names.
    i : str
        The name of the id variable.
    j : str
        The name of the subobservation variable.

    Returns
    -------
    DataFrame
        A DataFrame that contains each stub name as a variable as well as
        variables for i and j.

    Examples
    --------
    import pandas as pd
    import numpy as np
    np.random.seed(123)
    df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
                       "A1980" : {0 : "d", 1 : "e", 2 : "f"},
                       "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7},
                       "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1},
                       "X"     : dict(zip(range(3), np.random.randn(3)))
                      })
    df["id"] = df.index
    wide_to_long(df, ["A", "B"], i="id", j="year")

    Notes
    -----
    All extra variables are treated as extra id variables.
    """
    def get_var_names(df, regex):
        return df.filter(regex=regex).columns.tolist()

    def melt_stub(df, stub, i, j):
        varnames = get_var_names(df, "^"+stub)
        newdf = pd.melt(df, id_vars=i, value_vars=varnames, 
                         value_name=stub, var_name=j)
        newdf[j] = newdf[j].str.replace(stub, "").astype(int)
        return newdf

    id_vars = get_var_names(df, "^(?!%s)" % "|".join(stubnames))
    if i not in id_vars:
        id_vars += [i]

    stub = stubnames.pop(0)
    newdf = melt_stub(df, stub, id_vars, j)

    for stub in stubnames:
        new = melt_stub(df, stub, id_vars, j)
        newdf = newdf.merge(new, how="outer", on=id_vars + [j], copy=False)
    return newdf.set_index([i, j])

if __name__ == "__main__":
    url = "https://gist.github.com/jseabold/6652233/raw/44a04a78be85f5b3af16f26f51a143e047a10972/patr7079.csv"
    dta = pd.read_csv(url)
    dta["id"] = dta.index
    long_dta = wide_to_long(dta, ["LOGR", "PAT"], i="id", j="year")