pull_soi_partner.py

"""
SOI Partner Data (pull_soi_partner.py):
-------------------------------------------------------------
Module that handles reading in the soi partner data. Contains
one long script that loads in the capital stock, income and
entity information. Using the different asset and income
information (ratios of assets to income), data can be
allocated to all the industries in the different partner
entities.
"""

# Packages:
import re
import numpy as np
import pandas as pd
from ccc.utils import to_str
from data_paths import get_paths

globals().update(get_paths())

# Constants
_AST_FILE_FCTR = 10**3
_SHAPE = (131, 4)
_CODE_RANGE = ["32", "33", "45", "49"]
_PARENTS = {"32": "31", "33": "31", "45": "44", "49": "48"}


def load_partner_data(entity_dfs):
    """
    Reads in the partner data and creates new dataframes for
    each partner type and stores them in the soi dictionary.

    Args:
        entity_dfs (dictionary): dictionary of DataFrames with SOI data
            by entity type

    Returns:
        entity_dfs (dictionary): dictionary of DataFrames with SOI data
            by entity type
    """

    # with new cross walk:
    """
    1) read in partner data - 12pa03
    2) read in detailed xwalk to go from string names for
       industries to SOI codes
    3) In xwalk, keep only "complete = 1" codes
    3) Do an inner join of the two to keep only unique codes
    4) Sum over the ind code from crosswalk- because some
       repeated, those rep more detailed classifcations
       summing to larger SOI category
    5) Now have partner assets at as detailed industry codes
       as we can get
    6) Merge these partner codes to the soi_bea_ind_codes
       xwalk to get the sector, major, minor industry codes
            - Need to do 3 merges, 1 for those id's at two
              digit, 1 for those at 3 and one for those at 6
    7) Take corp data and summ assets by major ind to create
       ratios for each of 195 inds id'd in corp data - ratio
       of assets for that ind as a share of all in that major
       ind
    8) Merge these ratios to part data, will join on partner
       ind code to indy_cd from corp data and will have
       partner assets repeat for minor ind
    9) Multiply ratios by assets - unless part already at
       minor ind- should have what want now

    could do at major industry
    - do have 811 at minor industry
    """
    # Opening data on depreciable fixed assets, inventories,
    # and land for partnerhsips
    xwalk = pd.read_csv(_DETAIL_PART_CROSS_PATH)
    xwalk.rename({k: str(k) for k in xwalk.columns}, inplace=True)
    xwalk["Industry:"] = xwalk["Industry:"].apply(
        lambda x: re.sub(r"[\s+]", "", x)
    )
    # keep only codes that help to identify complete industries
    xwalk = xwalk[xwalk["complete"] == 1]
    # read in partner data - partner assets
    df = format_excel(pd.read_excel(_AST_FILE, skiprows=2, skipfooter=6))
    # Cuts off the repeated columns so only the data for all
    # partnerships remains
    df.index = [to_str(x) for x in df.index]
    xwalk.index = [to_str(x) for x in xwalk.index]
    df03 = df.T.groupby(sort=False, level=0).first().T
    # Fixing the index labels of the new dataframe
    df03.reset_index(inplace=True, drop=True)
    # Keep only variables of interest
    df03.columns = [to_str(c) for c in df03.columns]
    try:
        df03["Fixed Assets"] = (
            df03["Depreciable assets"]
            - df03["Less:  Accumulated depreciation"]
        )
    except Exception:
        print(df03.columns)
        raise
    df03 = df03[["Item", "Fixed Assets", "Inventories", "Land"]]
    df03["Item"] = df03["Item"].apply(lambda x: re.sub(r"[\s+]", "", x))

    # partner data - income
    df01 = format_excel(pd.read_excel(_INC_FILE, skiprows=2, skipfooter=6))
    # Cuts off the repeated columns so only the data for all
    # partnerships remains
    df01 = df01.T.groupby(sort=False, level=0).first().T
    df01.columns = [to_str(c) for c in df01.columns]
    # Fixing the index labels of the new dataframe
    df01.reset_index(inplace=True, drop=True)
    # Keep only variables of interest
    df01 = df01[["Item", "Depreciation"]]
    df01["Item old"] = df01["Item"].str.strip()
    df01["Item"] = df01["Item"].apply(lambda x: re.sub(r"[\s+]", "", x))

    # merge two partner data sources together so that all variables together
    df03 = df03.merge(df01, how="inner", on=["Item"], copy=True)

    # merge industry codes to partner data
    df03 = df03.merge(
        xwalk, how="inner", left_on=["Item"], right_on=["Industry:"], copy=True
    )
    df03.drop(
        ["Item", "Industry:", "Codes:", "Notes:", "complete"],
        axis=1,
        inplace=True,
    )

    # Sums together the repeated codes into one industry
    df03 = df03.groupby("INDY_CD", sort=False).sum()
    df03.reset_index(inplace=True)

    # create ratios for minor industry assets using corporate
    # data read in crosswalk for bea and soi industry codes
    soi_bea_ind_codes = pd.read_csv(
        _SOI_BEA_CROSS, dtype={"bea_ind_code": str}
    )
    soi_bea_ind_codes.columns = [to_str(c) for c in soi_bea_ind_codes.columns]
    soi_bea_ind_codes.drop("notes", axis=1, inplace=True)
    # drop one repeated minor ind code in crosswalk
    soi_bea_ind_codes.drop_duplicates(subset=["minor_code_alt"], inplace=True)

    # merge codes to partner data
    # likely better way to do this...
    df03_sector = df03[(df03["INDY_CD"] > 9) & (df03["INDY_CD"] < 100)]
    df03_major = df03[(df03["INDY_CD"] > 99) & (df03["INDY_CD"] < 1000)]
    df03_minor = df03[(df03["INDY_CD"] > 99999) & (df03["INDY_CD"] < 1000000)]
    sector_df = df03_sector.merge(
        soi_bea_ind_codes,
        how="inner",
        left_on=["INDY_CD"],
        right_on=["sector_code"],
        copy=True,
        indicator=True,
    )
    major_df = df03_major.merge(
        soi_bea_ind_codes,
        how="inner",
        left_on=["INDY_CD"],
        right_on=["major_code"],
        copy=True,
        indicator=True,
    )
    minor_df = df03_minor.merge(
        soi_bea_ind_codes,
        how="inner",
        left_on=["INDY_CD"],
        right_on=["minor_code"],
        copy=True,
        indicator=True,
    )
    part_data = (
        sector_df.append([major_df, minor_df], sort=True, ignore_index=True)
        .copy()
        .reset_index()
    )
    part_data.drop(
        ["bea_inv_name", "bea_code", "_merge"], axis=1, inplace=True
    )

    # merge codes to total corp data
    # inner join means that we keep only rows that match in both datasets
    # this should keep only unique soi minor industries
    # in total corp data - note that s corp data already unique by sector
    columns = ["Fixed Assets", "Inventories", "Land", "Depreciation"]
    s_corp = entity_dfs["s_corp"][["minor_code_alt"] + columns]
    corp = s_corp.merge(
        soi_bea_ind_codes,
        how="inner",
        on=["minor_code_alt"],
        suffixes=("_x", "_y"),
        copy=True,
    )
    for var in columns:
        corp[var + "_ratio"] = corp.groupby(["major_code"])[var].apply(
            lambda x: x / float(x.sum())
        )

    corp.drop(
        ["bea_inv_name", "bea_code", "sector_code", "minor_code"] + columns,
        axis=1,
        inplace=True,
    )

    # merge these ratios to the partner data
    part_data = part_data.merge(
        corp,
        how="right",
        on=["minor_code_alt"],
        suffixes=("_x", "_y"),
        copy=True,
    )

    # allocate capital based on ratios
    for var in columns:
        part_data[var] = part_data[var] * part_data[var + "_ratio"]

    part_data.drop(list(x + "_ratio" for x in columns), axis=1, inplace=True)
    part_data.drop(
        [
            "index",
            "sector_code",
            "major_code_x",
            "minor_code",
            "INDY_CD",
            "major_code_y",
        ],
        axis=1,
        inplace=True,
    )

    # !!! Partner data has right industry breakouts, and ratio sum to
    # 1 in ind, but totals not adding up to SOI controls. Not quite
    # sure why. But within  one percent of total except for fix assests
    # off by 7%.  Going forward with this- it may be that industry
    # splits aren't adding to SOI control total

    # Attribute by partner type

    # Read in data by partner type (gives income allocation by partner type)
    df05 = format_excel(pd.read_excel(_TYP_FILE, skiprows=1, skipfooter=5))
    df05.columns = [to_str(c) for c in df05.columns]
    df05 = df05[
        [
            "Item",
            "All partners",
            "Corporate general partners",
            "Corporate limited partners",
            "Individual general partners",
            "Individual limited partners",
            "Partnership general partners",
            "Partnership limited partners",
            "Tax-exempt organization general partners",
            "Tax-exempt organization limited partners",
            "Nominee and other general partners",
            "Nominee and other limited partners",
        ]
    ]
    # # create dictionary with shorter part type names
    part_types = {
        "Corporate general partners": "corp_gen",
        "Corporate limited partners": "corp_lim",
        "Individual general partners": "indv_gen",
        "Individual limited partners": "indv_lim",
        "Partnership general partners": "prt_gen",
        "Partnership limited partners": "prt_lim",
        "Tax-exempt organization general partners": "tax_gen",
        "Tax-exempt organization limited partners": "tax_lim",
        "Nominee and other general partners": "nom_gen",
        "Nominee and other limited partners": "nom_lim",
    }

    # reshape data
    df05 = pd.melt(
        df05,
        id_vars=["Item"],
        value_vars=[
            "Corporate general partners",
            "Corporate limited partners",
            "Individual general partners",
            "Individual limited partners",
            "Partnership general partners",
            "Partnership limited partners",
            "Tax-exempt organization general partners",
            "Tax-exempt organization limited partners",
            "Nominee and other general partners",
            "Nominee and other limited partners",
        ],
        var_name="part_type",
        value_name="net_inc",
    )

    # merge in codes
    typ_cross = pd.read_csv(_TYP_IN_CROSS_PATH)
    typ_cross.columns = [to_str(c) for c in typ_cross.columns]
    typ_cross["Industry:"] = typ_cross["Industry:"].str.strip()
    df05["Item"] = df05["Item"].str.strip()
    df05 = df05.merge(
        typ_cross,
        how="inner",
        left_on=["Item"],
        right_on=["Industry:"],
        copy=True,
    )

    # # create sums by group
    grouped = pd.DataFrame(
        {"sum": df05.groupby(["Codes:"]).apply(abs_sum, "net_inc")}
    ).reset_index()
    # merge grouped data back to original df
    # One could make this more efficient - one line of code - with appropriate
    # pandas methods using groupby and apply above
    df05 = df05.merge(grouped, how="left", on=["Codes:"], copy=True)
    df05["inc_ratio"] = (
        df05["net_inc"].astype(float).abs() / df05["sum"].replace({0: np.nan})
    ).replace({np.nan: 0})
    df05 = df05[["Codes:", "part_type", "net_inc", "inc_ratio"]]

    # manufacturing is missing data for 2013, so use overall partnership splits
    for key in part_types:
        df05.loc[
            (df05["Codes:"] == 31) & (df05["part_type"] == key), "inc_ratio"
        ] = df05.loc[
            (df05["Codes:"] == 1) & (df05["part_type"] == key), "inc_ratio"
        ].values
    # add other sector codes for manufacturing
    manu = df05[df05["Codes:"] == 31]
    df_manu = (manu.append(manu, sort=True)).reset_index(drop=True)
    df_manu.loc[: len(part_types), "Codes:"] = 32
    df_manu.loc[len(part_types) :, "Codes:"] = 33
    df05 = (
        df05.append(df_manu, sort=True, ignore_index=True)
        .reset_index(drop=True)
        .copy()
    )
    # # Merge SOI codes to BEA data
    df05_sector = df05[(df05["Codes:"] > 9) & (df05["Codes:"] < 100)]
    df05_major = df05[(df05["Codes:"] > 99) & (df05["Codes:"] < 1000)]
    df05_minor = df05[(df05["Codes:"] > 99999) & (df05["Codes:"] < 1000000)]
    sector_df = df05_sector.merge(
        soi_bea_ind_codes,
        how="inner",
        left_on=["Codes:"],
        right_on=["sector_code"],
        copy=True,
        indicator=True,
    )
    major_df = df05_major.merge(
        soi_bea_ind_codes,
        how="inner",
        left_on=["Codes:"],
        right_on=["major_code"],
        copy=True,
        indicator=True,
    )
    minor_df = df05_minor.merge(
        soi_bea_ind_codes,
        how="inner",
        left_on=["Codes:"],
        right_on=["minor_code"],
        copy=True,
        indicator=True,
    )
    df05 = (
        sector_df.append([major_df, minor_df], sort=True, ignore_index=True)
        .copy()
        .reset_index()
    )
    df05.drop(["bea_inv_name", "bea_code", "_merge"], axis=1, inplace=True)

    # # merge partner type ratios with partner asset data
    part_assets = df05.merge(
        part_data, how="left", on=["minor_code_alt"], copy=True, indicator=True
    )
    part_assets.drop(["Codes:", "_merge"], axis=1, inplace=True)

    # allocate across partner type
    part_assets["Fixed Assets"] = (
        part_assets["Fixed Assets"] * part_assets["inc_ratio"]
    )
    part_assets["Inventories"] = (
        part_assets["Inventories"] * part_assets["inc_ratio"]
    )
    part_assets["Land"] = part_assets["Land"] * part_assets["inc_ratio"]
    part_assets["Depreciation"] = (
        part_assets["Depreciation"] * part_assets["inc_ratio"]
    )
    part_data = {"part_data": part_assets}

    return part_data


def abs_sum(group, avg_name):
    """
    Computes the sum of absolute values within a group.

    Args:
        group (Pandas groupby object): grouping
        avg_name (string): variable name to take sum over

    Returns:
        summand (Pandas groupby object): grouping
    """
    d = group[avg_name]
    summand = (np.absolute(d)).sum()

    return summand


def format_excel(df):
    """
    Formats an excel file.

    Args:
        df (DataFrame): table of data read in from an Excel file

    Returns:
        df (DataFrame): formatted DataFrame
    """

    for i, element in enumerate(df.iloc[0, :]):
        j = 1
        while isinstance(element, float):
            element = df.iloc[j, :][i]
            j += 1
        df.iloc[0, :][i] = element.replace("\n", " ").replace("  ", " ")
    df.dropna(inplace=True)
    df = df.T
    column_names = df.iloc[0, :].tolist()
    column_names = [
        x.encode("ascii", "ignore").lstrip().rstrip() for x in column_names
    ]
    df.columns = column_names
    df = df.drop(df.index[[0, len(df) - 1]])
    df = df.fillna(0)
    df = df.replace("[d]", 0)
    df = df.replace("[d]  ", 0)
    df = df.replace("[2]  ", 0)
    df.reset_index(inplace=True, drop=True)
    df.iloc[:, 1:] = df.iloc[:, 1:] * _AST_FILE_FCTR

    return df