Coming back to this after 2 years, here is a much better practice:
from itertools import product, chain
import pandas as pd
import numpy as np
from typing import Dict
def calc_col_names(named_shape):
*prefix, shape = named_shape
names = [map(str, range(i)) for i in shape]
return map('_'.join, product(prefix, *names))
def create_flat_columns_df_from_dict_of_numpy(
named_np: Dict[str, np.array],
n_samples_per_np: int,
):
named_np_correct_lenth = {k: v for k, v in named_np.items() if len(v) == n_samples_per_np}
flat_nps = [a.reshape(n_samples_per_np, -1) for a in named_np_correct_lenth.values()]
stacked_nps = np.column_stack(flat_nps)
named_shapes = [(name, arr.shape[1:]) for name, arr in named_np_correct_lenth.items()]
col_names = [*chain.from_iterable(calc_col_names(named_shape) for named_shape in named_shapes)]
df = pd.DataFrame(stacked_nps, columns=col_names)
df = df.convert_dtypes()
return df
def parse_series_into_np(df, col_name, shp):
# can parse the shape from the col names
n_samples = len(df)
col_names = sorted(c for c in df.columns if col_name in c)
col_names = list(filter(lambda c: c.startswith(col_name + "_") or len(col_names) == 1, col_names))
col_as_np = df[col_names].astype(np.float).values.reshape((n_samples, *shp))
return col_as_np
usage to put a ndarray into a Dataframe:
full_rate_df = create_flat_columns_df_from_dict_of_numpy(
named_np={name: np.array(d[name]) for name in ["name1", "name2"]},
n_samples_per_np=d["name1"].shape[0]
)
where d is a dict of nd arrays of the same shape[0], hashed by ["name1", "name2"].
The reverse operation can be obtained by parse_series_into_np.
The accepted answer remains, as it answers the original question, but this one is a much better practice.
