df[x] — index a column using variable x. Returns pd.Series df[[x]] — index/slice a single-column DataFrame using variable x. Returns pd.DataFrame df[‘x’] — index a column named ‘x’. Returns pd.Series df[[‘x’]] — index/slice a single-column DataFrame having only one column named ‘x’. Returns pd.DataFrame df.x — dot accessor notation, equivalent to df[‘x’] (there are, however, … Read more
Use sort_values, i.e. means = means.sort_values(). [Pandas v0.17+] (Very old answer, pre-v0.17 / 2015) pandas used to use order() method: means = means.order().
You can use the dayfirst parameter in pd.to_datetime. pd.to_datetime(df.tmp, dayfirst=True) Output: 0 2015-06-11 00:56:55 1 2015-06-11 04:16:38 2 2015-06-12 16:13:30 3 2015-06-12 21:24:03 4 2015-06-13 02:31:44 5 2015-06-13 02:37:49 Name: tmp, dtype: datetime64[ns]
Two easy ways to accomplish just the filtering are: Using filter: names = filter(lambda name: name[-5:] != “Smith”, names) Using list comprehensions: names = [name for name in names if name[-5:] != “Smith”] Note that both cases keep the values for which the predicate function evaluates to True, so you have to reverse the logic … Read more
I also like itertuples() for row in df.itertuples(): print(row.A) print(row.Index) since row is a named tuples, if you meant to access values on each row this should be MUCH faster speed run : df = pd.DataFrame([x for x in range(1000*1000)], columns=[‘A’]) st=time.time() for index, row in df.iterrows(): row.A print(time.time()-st) 45.05799984931946 st=time.time() for row in df.itertuples(): … Read more
You can join a string on the series directly: In [3]: ‘ ‘.join(df[‘text’]) Out[3]: ‘Hello world !’
For python 3.8 try: def f() -> “pd.Series[str]”: pass or: f_return_type = “pd.Series[str]” def f() -> f_return_type: pass or # type: pd.Series[str] for variables
You can just use map: In [7]: df = pd.DataFrame({‘Status’:[‘Delivered’, ‘Delivered’, ‘Undelivered’, ‘SomethingElse’]}) In [8]: df Out[8]: Status 0 Delivered 1 Delivered 2 Undelivered 3 SomethingElse In [9]: d = {‘Delivered’: True, ‘Undelivered’: False} In [10]: df[‘Status’].map(d) Out[10]: 0 True 1 True 2 False 3 NaN Name: Status, dtype: object
use map to perform a lookup: In [46]: df[‘1st’] = df[‘1st’].map(idxDict) df Out[46]: 1st 2nd 0 a 2 1 b 4 2 c 6 to avoid the situation where there is no valid key you can pass na_action=’ignore’ You can also use df[‘1st’].replace(idxDict) but to answer you question about efficiency: timings In [69]: %timeit df[‘1st’].replace(idxDict) … Read more
Using Boolean Indexing >>> s = pd.Series([True, False, True, True, False, False, False, True]) >>> s[s].index Int64Index([0, 2, 3, 7], dtype=”int64″) If need a np.array object, get the .values >>> s[s].index.values array([0, 2, 3, 7]) Using np.nonzero >>> np.nonzero(s) (array([0, 2, 3, 7]),) Using np.flatnonzero >>> np.flatnonzero(s) array([0, 2, 3, 7]) Using np.where >>> np.where(s)[0] … Read more