Granted that the behavior is inconsistent, but I think it’s easy to imagine cases where this is convenient. Anyway, to get a DataFrame every time, just pass a list to loc. There are other ways, but in my opinion this is the cleanest. In [2]: type(df.loc[[3]]) Out[2]: pandas.core.frame.DataFrame In [3]: type(df.loc[[1]]) Out[3]: pandas.core.frame.DataFrame
Rather than create 2 temporary dfs you can just pass these as params within a dict using the DataFrame constructor: pd.DataFrame({’email’:sf.index, ‘list’:sf.values}) There are lots of ways to construct a df, see the docs
I think concat is a nice way to do this. If they are present it uses the name attributes of the Series as the columns (otherwise it simply numbers them): In [1]: s1 = pd.Series([1, 2], index=[‘A’, ‘B’], name=”s1″) In [2]: s2 = pd.Series([3, 4], index=[‘A’, ‘B’], name=”s2″) In [3]: pd.concat([s1, s2], axis=1) Out[3]: s1 … Read more
method 1 sample.housing.eq(‘yes’).mul(1) method 2 pd.Series(np.where(sample.housing.values == ‘yes’, 1, 0), sample.index) method 3 sample.housing.map(dict(yes=1, no=0)) method 4 pd.Series(map(lambda x: dict(yes=1, no=0)[x], sample.housing.values.tolist()), sample.index) method 5 pd.Series(np.searchsorted([‘no’, ‘yes’], sample.housing.values), sample.index) All yield 0 0 1 0 2 1 3 0 4 0 5 0 6 0 7 0 8 1 9 1 timing given sample timing … Read more
Explicit is better than implicit. df[boolean_mask] selects rows where boolean_mask is True, but there is a corner case when you might not want it to: when df has boolean-valued column labels: In [229]: df = pd.DataFrame({True:[1,2,3],False:[3,4,5]}); df Out[229]: False True 0 3 1 1 4 2 2 5 3 You might want to use df[[True]] … Read more
I suspect you are adding the shape annotations to the plot, where they are drawn last. Instead, add them to the renderer in Layer.BACKGROUND. As shown below, the circle does not obscure the tool tip at (20, 20). Note also how (10, 10) is not affected by the line annotation, while (30, 30) is obscured … Read more
You need to check all numbers from 2 to n-1 (to sqrt(n) actually, but ok, let it be n). If n is divisible by any of the numbers, it is not prime. If a number is prime, print it. for num in range(2,101): prime = True for i in range(2,num): if (num%i==0): prime = False … Read more
DataFrame.append is not an in-place operation. From the docs, DataFrame.append(other, ignore_index=False, verify_integrity=False, sort=None) Append rows of other to the end of this frame, returning a new object. Columns not in this frame are added as new columns. You need to assign the result back. df8 = df8.append([s] * 2, ignore_index=True) df8 A B C D … Read more
First, change the type of the column: df.cc = pd.Categorical(df.cc) Now the data look similar but are stored categorically. To capture the category codes: df[‘code’] = df.cc.cat.codes Now you have: cc temp code 0 US 37.0 2 1 CA 12.0 1 2 US 35.0 2 3 AU 20.0 0 If you don’t want to modify … Read more
The dtype object comes from NumPy, it describes the type of element in a ndarray. Every element in an ndarray must have the same size in bytes. For int64 and float64, they are 8 bytes. But for strings, the length of the string is not fixed. So instead of saving the bytes of strings in … Read more