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332 def 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)
39 def _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)
3169 def 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)
259 def drop_some(df_: pd.DataFrame, thresh: int) -> pd.DataFrame: 260 # thresh is the minimum number of NA, the 1 indicates that columns should be dropped not rows 261 return df_.dropna(1, thresh=thresh)