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46 changes: 16 additions & 30 deletions ipysheet/pandas_loader.py
Original file line number Diff line number Diff line change
@@ -1,21 +1,11 @@
from .sheet import Cell, Sheet
from .utils import extract_data, get_cell_numeric_format, get_cell_type

def _format_date(column):
return column.dt.strftime('%Y/%m%/%d')

def _format_date(date):
import pandas as pd

return pd.to_datetime(str(date)).strftime('%Y/%m/%d')


def _get_cell_value(arr):
import pandas as pd

if (arr.dtype.kind == 'M'):
return [_format_date(date) if not pd.isna(date) else None for date in arr]
else:
return arr.tolist()

def _get_cell_values(column, dtype):
return (_format_date(column) if dtype.kind == 'M' else column).values

def from_dataframe(dataframe):
""" Helper function for creating a sheet out of a Pandas DataFrame
Expand All @@ -37,37 +27,33 @@ def from_dataframe(dataframe):
>>> sheet = from_dataframe(df)
>>> display(sheet)
"""
import numpy as np

# According to pandas documentation: "NumPy arrays have one dtype for the
# entire array, while pandas DataFrames have one dtype per column", so it
# makes more sense to create the sheet and fill it column-wise
columns = dataframe.columns.tolist()
rows = dataframe.index.tolist()
cells = []

idx = 0
for c in columns:
arr = np.array(dataframe[c].values)
for c in dataframe.columns:
idx = dataframe.columns.get_loc(c)
dtype = dataframe.dtypes[c]
cells.append(Cell(
value=_get_cell_value(arr),
value=_get_cell_values(dataframe[c], dtype),
row_start=0,
row_end=len(rows) - 1,
row_end=len(dataframe.index) - 1,
column_start=idx,
column_end=idx,
type=get_cell_type(arr.dtype),
numeric_format=get_cell_numeric_format(arr.dtype),
type=get_cell_type(dtype),
numeric_format=get_cell_numeric_format(dtype),
squeeze_row=False,
squeeze_column=True
))
idx += 1

return Sheet(
rows=len(rows),
columns=len(columns),
rows=len(dataframe.index),
columns=len(dataframe.columns),
cells=cells,
row_headers=[str(header) for header in rows],
column_headers=[str(header) for header in columns]
row_headers=dataframe.index.astype(str).tolist(),
column_headers=dataframe.columns.astype(str).tolist()
)


Expand Down