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import itertools
from typing import Dict, List
class DataFrame:
"""Table-like class for storing a 2D cells table with named columns."""
def __init__(self, data: Dict[str, List[object]] = {}):
"""
Create a new DataFrame from a dictionary (keys = headers,
values = columns).
"""
self._headers = [i for i in data.keys()]
self._rows = []
row_num = 0
def get_data_row(idx):
r = {}
for header, header_data in data.items():
if not len(header_data) > idx:
continue
r[header] = header_data[idx]
return r
while True:
row_dict = get_data_row(row_num)
if len(row_dict) == 0:
break
self._append_row(row_dict.keys(), row_dict.values())
row_num = row_num + 1
def concat_rows(self, other: 'DataFrame') -> None:
"""
In-place concatenate rows of other into the rows of the
current DataFrame.
None is added in pre-existing cells if new headers
are introduced.
"""
other_datas = other._data_only()
other_headers = other.headers
for d in other_datas:
self._append_row(other_headers, d)
def _append_row(self, headers: List[str], data: List[object]):
new_row = {k:v for k,v in zip(headers, data)}
self._rows.append(new_row)
for header in headers:
if not header in self._headers:
self._headers.append(header)
def __repr__(self):
# return repr(self._rows)
repr = ""
header_list = self._headers_only()
row_format = u""
for header in header_list:
row_format = row_format + u"{:>%d}" %(len(header) + 1)
repr = row_format.format(*header_list) + "\n"
for v in self._data_only():
repr = repr + row_format.format(*v) + "\n"
return repr
def __eq__(self, other):
if isinstance(other, self.__class__):
return self.headers == other.headers and self.data_table == other.data_table
else:
print("wrong instance", other.__class__)
return False
@property
def headers(self) -> List[str]:
return [i for i in self._headers_only()]
@property
def data_table(self) -> List[List[object]]:
return list(self._data_only())
@property
def data_table_transposed(self) -> List[List[object]]:
return list(self._transposed_data())
@property
def data_row_len(self) -> int:
return len(self._rows)
def data_row_at(self, idx) -> List[object]:
"""
Return a single data row at the specified index (0th based).
Accepts negative indices, e.g. -1 is last row.
"""
row_dict = self._rows[idx]
l = []
for h in self._headers_only():
l.append(row_dict.get(h)) # Adds None in blank spots.
return l
def copy(self) -> 'DataFrame':
"""
Shallow copy of this DataFrame.
"""
return self.repeat(count=0)
def repeat(self, count: int) -> 'DataFrame':
"""
Returns a new DataFrame where each row of this dataframe is repeated count times.
A repeat of a row is adjacent to other repeats of that same row.
"""
df = DataFrame()
df._headers = self._headers.copy()
rows = []
for row in self._rows:
for i in range(count):
rows.append(row.copy())
df._rows = rows
return df
def merge_data_columns(self, other: 'DataFrame'):
"""
Merge self and another DataFrame by adding the data from other column-wise.
For any headers that are the same, data from 'other' is preferred.
"""
for h in other._headers:
if not h in self._headers:
self._headers.append(h)
append_rows = []
for self_dict, other_dict in itertools.zip_longest(self._rows, other._rows):
if not self_dict:
d = {}
append_rows.append(d)
else:
d = self_dict
d_other = other_dict
if d_other:
for k,v in d_other.items():
d[k] = v
for r in append_rows:
self._rows.append(r)
def data_row_reduce(self, fnc) -> 'DataFrame':
"""
Reduces the data row-wise by applying the fnc to each row (column-wise).
Empty cells are skipped.
fnc(Iterable[object]) -> object
fnc is applied over every non-empty cell in that column (descending row-wise).
Example:
DataFrame({'a':[1,2,3]}).data_row_reduce(sum) == DataFrame({'a':[6]})
Returns a new single-row DataFrame.
"""
df = DataFrame()
df._headers = self._headers.copy()
def yield_by_column(header_key):
for row_dict in self._rows:
val = row_dict.get(header_key)
if val:
yield val
new_row_dict = {}
for h in df._headers:
cell_value = fnc(yield_by_column(h))
new_row_dict[h] = cell_value
df._rows = [new_row_dict]
return df
def _headers_only(self):
return self._headers
def _data_only(self):
row_len = len(self._rows)
for i in range(row_len):
yield self.data_row_at(i)
def _transposed_data(self):
return zip(*self._data_only())
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