where

来源： https://stackoverflow.com/questions/47513408/pandas-dataframe-check-if-column-value-is-in-column-list

来源： https://stackoverflow.com/questions/47513408/pandas-dataframe-check-if-column-value-is-in-column-list

问题 I have an array e.g. a = [5,1,3,0,2] I apply the where function: np.where(a == 2) The output is an empty array (array([], dtype=int64),) I found kind of the same problem here, but in my case it really dosen't make any sense or dose it? Btw. i'm on a Mac using Python 2.7.10 回答1: You're passing a list to where() function not a Numpy array. Use an array instead: In [20]: a = np.array([5,1,3,0,2]) In [21]: np.where(a == 2) Out[21]: (array([4]),) Also as mentioned in comments, in this case the

问题 Edit: talking with a_horse_with_no_name I found that "IS" is a little bit different in SQLite allowing comparisons between NULL and values using "IS": stackoverflow.com/a/9102445/1470058. This clears up a lot of confusion for me. Thanks: The IS and IS NOT operators work like = and != except when one or both of the operands are NULL. In this case, if both operands are NULL, then the IS operator evaluates to 1 (true) and the IS NOT operator evaluates to 0 (false). If one operand is NULL and the

问题 For inner joins , is there any difference in performance to apply a filter in the JOIN ON clause or the WHERE clause? Which is going to be more efficient, or will the optimizer render them equal? JOIN ON SELECT u.name FROM users u JOIN departments d ON u.department_id = d.id AND d.name = 'IT' VS WHERE SELECT u.name FROM users u JOIN departments d ON u.department_id = d.id WHERE d.name = 'IT' Oracle 11gR2 回答1: There should be no difference. The optimizer should generate the same plan in both

问题 For inner joins , is there any difference in performance to apply a filter in the JOIN ON clause or the WHERE clause? Which is going to be more efficient, or will the optimizer render them equal? JOIN ON SELECT u.name FROM users u JOIN departments d ON u.department_id = d.id AND d.name = 'IT' VS WHERE SELECT u.name FROM users u JOIN departments d ON u.department_id = d.id WHERE d.name = 'IT' Oracle 11gR2 回答1: There should be no difference. The optimizer should generate the same plan in both

问题 All the research I do point to using loc as the way to filter a dataframe by a col(s) value(s), today I was reading this and I discovered by the examples I tested, that loc isn't isn't really needed when filtering cols by it's values: EX: df = pd.DataFrame(np.arange(0, 20, 0.5).reshape(8, 5), columns=['a', 'b', 'c', 'd', 'e']) df.loc[df['a'] >= 15] a b c d e 6 15.0 15.5 16.0 16.5 17.0 7 17.5 18.0 18.5 19.0 19.5 df[df['a'] >= 15] a b c d e 6 15.0 15.5 16.0 16.5 17.0 7 17.5 18.0 18.5 19.0 19.5

问题 Is it possible to convert an array of indices to an array of ones and zeros, given the range? i.e. [2,3] -> [0, 0, 1, 1, 0], in range of 5 I'm trying to automate something like this: >>> index_array = np.arange(200,300) array([200, 201, ... , 299]) >>> mask_array = ??? # some function of index_array and 500 array([0, 0, 0, ..., 1, 1, 1, ... , 0, 0, 0]) >>> train(data[mask_array]) # trains with 200~299 >>> predict(data[~mask_array]) # predicts with 0~199, 300~499 回答1: Here's one way: In [1]:

问题 I came up against a strange problem in Postgres yesterday when trying to filter out user ids from a stats table. When we did, for example, user_id != 24 , postgres excluded the rows where user_id is NULL as well. I created the following test code which shows the same results. CREATE TEMPORARY TABLE test1 ( id int DEFAULT NULL ); INSERT INTO test1 (id) VALUES (1), (2), (3), (4), (5), (2), (4), (6), (4), (7), (5), (9), (5), (3), (6), (4), (3), (7), (NULL), (NULL), (NULL), (NULL), (NULL), (NULL)

问题 I came up against a strange problem in Postgres yesterday when trying to filter out user ids from a stats table. When we did, for example, user_id != 24 , postgres excluded the rows where user_id is NULL as well. I created the following test code which shows the same results. CREATE TEMPORARY TABLE test1 ( id int DEFAULT NULL ); INSERT INTO test1 (id) VALUES (1), (2), (3), (4), (5), (2), (4), (6), (4), (7), (5), (9), (5), (3), (6), (4), (3), (7), (NULL), (NULL), (NULL), (NULL), (NULL), (NULL)