10 examples of 'pandas fillna with another column' in Python

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114def panel_fillna(panel, type="bfill"):
115 """
116 fill nan along the 3rd axis
117 :param panel: the panel to be filled
118 :param type: bfill or ffill
119 """
120 frames = {}
121 for item in panel.items:
122 if type == "both":
123 frames[item] = panel.loc[item].fillna(axis=1, method="bfill").\
124 fillna(axis=1, method="ffill")
125 else:
126 frames[item] = panel.loc[item].fillna(axis=1, method=type)
127 return pd.Panel(frames)
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19def fill_missing_values(df_data):
20 """Fill missing values in data frame, in place."""
21 df_data.fillna(method="ffill", inplace="True")
22 df_data.fillna(method='bfill', inplace="True")
39def _drop_col(self, df):
40 '''
41 Drops last column, which was added in the parsing procedure due to a
42 trailing white space for each sample in the text file
43 Arguments:
44 df: pandas dataframe
45 Return:
46 df: original df with last column dropped
47 '''
48 return df.drop(df.columns[-1], axis=1)
3169def hpat_pandas_series_dropna_impl(self, axis=0, inplace=False):
3170 # generate Series index if needed by using SeriesType.index (i.e. not self._index)
3171 na_data_arr = hpat.hiframes.api.get_nan_mask(self._data)
3172 data = self._data[~na_data_arr]
3173 index = self.index[~na_data_arr]
3174 return pandas.Series(data, index, self._name)
2413def combine_first(self, other):
2414 """Combine two Datasets, default to data_vars of self.
2415
2416 The new coordinates follow the normal broadcasting and alignment rules
2417 of ``join='outer'``. Vacant cells in the expanded coordinates are
2418 filled with np.nan.
2419
2420 Parameters
2421 ----------
2422 other : DataArray
2423 Used to fill all matching missing values in this array.
2424
2425 Returns
2426 -------
2427 DataArray
2428 """
2429 out = ops.fillna(self, other, join="outer", dataset_join="outer")
2430 return out
38def fill_empty(df: pd.DataFrame) -> pd.DataFrame:
39 """Fill the gaps in the 'original' column with values from 'index'
40 Args:
41 df:
42 Returns:
43 """
44 index = df["original"].isnull()
45 df.loc[index, "original"] = df.loc[index, "index"]
46
47 return df
580def sdc_fillna_int_impl(self, inplace=False, value=None):
581 return copy(self)
332def dropcols(df, start=None, end=None):
333 """Drop columns that contain NaN within [start, end] inclusive.
334
335 A wrapper around DataFrame.dropna() that builds an easier *subset*
336 syntax for tseries-indexed DataFrames.
337
338 Parameters
339 ----------
340 df : DataFrame
341 start : str or datetime, default None
342 start cutoff date, inclusive
343 end : str or datetime, default None
344 end cutoff date, inclusive
345
346 Example
347 -------
348 df = DataFrame(np.random.randn(10,3),
349 index=pd.date_range('2017', periods=10))
350
351 # Drop in some NaN
352 df.set_value('2017-01-04', 0, np.nan)
353 df.set_value('2017-01-02', 2, np.nan)
354 df.loc['2017-01-05':, 1] = np.nan
355
356 # only col2 will be kept--its NaN value falls before `start`
357 print(dropcols(df, start='2017-01-03'))
358 2
359 2017-01-01 0.12939
360 2017-01-02 NaN
361 2017-01-03 0.16596
362 2017-01-04 1.06442
363 2017-01-05 -1.87040
364 2017-01-06 -0.17160
365 2017-01-07 0.94588
366 2017-01-08 1.49246
367 2017-01-09 0.02042
368 2017-01-10 0.75094
369
370 """
371
372 if isinstance(df, Series):
373 raise ValueError("func only applies to `pd.DataFrame`")
374 if start is None:
375 start = df.index[0]
376 if end is None:
377 end = df.index[-1]
378 subset = df.index[(df.index >= start) & (df.index <= end)]
379 return df.dropna(axis=1, subset=subset)
29def _concat(df, type):
30 if df is None:
31 df = pd.DataFrame(_object_blocks[type])
32 else:
33 _df = pd.DataFrame(_object_blocks[type])
34 df = pd.concat([df, _df], sort=True)
35 return df
813def merge(old_cols, new_cols):
814 return old_cols + new_cols

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