<gh_stars>0
import pandas as pd


class MergeCountsAndMetadata:
    """
    On both the counts and metadata dataframes, we assume that the different samples
    are displayed horizontally and the features(or taxa) vertically:

    samples  1  2  3
    feature1 x  t  c
    feature2 y  g  a
    feature3 v  b  n
    """

    def __init__(self, metadata_df, reads_df):
        self.metadata_df = metadata_df
        self.read_count_df = reads_df
        self.metadata_df.columns = self.metadata_df.columns.astype(str)
        self.read_count_df.columns = self.read_count_df.columns.astype(str)

    def check_column_names(self):
        return self.metadata_df.columns.equals(self.read_count_df.columns)

    @property
    def full_dataframe(self):
        if getattr(self, "_full_dataframe", None) is None:
            if self.check_column_names() is True:
                meta_and_reads_df = pd.concat([self.metadata_df, self.read_count_df], axis=0, sort=False)
                setattr(self, "_full_dataframe", meta_and_reads_df)
            else:
                raise Exception('Cannot concat metadata dataframe and reads dataframe because\
                   column names or number of samples do not match.')
        return self._full_dataframe

    @property
    def full_dataframe_with_features_in_columns(self):
        if getattr(self, "_full_dataframe_with_features_in_columns", None) is None:
            full_dataframe_with_features_in_columns = self.full_dataframe.transpose()
            setattr(self, "_full_dataframe_with_features_in_columns", full_dataframe_with_features_in_columns)
        return self._full_dataframe_with_features_in_columns

import pandas as pd


class MergeCountsAndMetadata:
    """
    On both the counts and metadata dataframes, we assume that the different samples
    are displayed horizontally and the features(or taxa) vertically:

    samples  1  2  3
    feature1 x  t  c
    feature2 y  g  a
    feature3 v  b  n
    """

    def __init__(self, metadata_df, reads_df):
        self.metadata_df = metadata_df
        self.read_count_df = reads_df
        self.metadata_df.columns = self.metadata_df.columns.astype(str)
        self.read_count_df.columns = self.read_count_df.columns.astype(str)

    def check_column_names(self):
        return self.metadata_df.columns.equals(self.read_count_df.columns)

    @property
    def full_dataframe(self):
        if getattr(self, "_full_dataframe", None) is None:
            if self.check_column_names() is True:
                meta_and_reads_df = pd.concat([self.metadata_df, self.read_count_df], axis=0, sort=False)
                setattr(self, "_full_dataframe", meta_and_reads_df)
            else:
                raise Exception('Cannot concat metadata dataframe and reads dataframe because\
                   column names or number of samples do not match.')
        return self._full_dataframe

    @property
    def full_dataframe_with_features_in_columns(self):
        if getattr(self, "_full_dataframe_with_features_in_columns", None) is None:
            full_dataframe_with_features_in_columns = self.full_dataframe.transpose()
            setattr(self, "_full_dataframe_with_features_in_columns", full_dataframe_with_features_in_columns)
        return self._full_dataframe_with_features_in_columns
On both the counts and metadata dataframes, we assume that the different samples     are displayed horizontally and the features(or taxa) vertically:      samples  1  2  3     feature1 x  t  c     feature2 y  g  a     feature3 v  b  n