aggregate

问题 [{'_id': '5ebe39e41e1729d90de', 'modelId': '5ebe3536c289711579', 'lastAt': datetime.datetime(2020, 5, 15, 6, 42, 44, 79000), 'proId': '5ebe3536c2897115793dccfb', 'genId': '5ebe355ac2897115793dcd04'}, {'_id': '5ebe3a0d94fcb800fa474310', 'modelId': '5ebe3536c289711579', 'proId': '5ebe3536c2897115793d', 'genId': '5ebe355ac2897115793', 'lastAt': datetime.datetime(2020, 5, 15, 6, 43, 25, 105000)}] I want to calculate count of hits for a day, for last 7 days and for a months. As the model is same,

问题 [{'_id': '5ebe39e41e1729d90de', 'modelId': '5ebe3536c289711579', 'lastAt': datetime.datetime(2020, 5, 15, 6, 42, 44, 79000), 'proId': '5ebe3536c2897115793dccfb', 'genId': '5ebe355ac2897115793dcd04'}, {'_id': '5ebe3a0d94fcb800fa474310', 'modelId': '5ebe3536c289711579', 'proId': '5ebe3536c2897115793d', 'genId': '5ebe355ac2897115793', 'lastAt': datetime.datetime(2020, 5, 15, 6, 43, 25, 105000)}] I want to calculate count of hits for a day, for last 7 days and for a months. As the model is same,

问题 Given a DataFrame with an ID column and corresponding values column, how can I aggregate (let's say sum) the values within blocks of repeating IDs? Example DF: import numpy as np import pandas as pd df = pd.DataFrame( {'id': ['a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'a', 'a', 'b', 'a', 'b', 'b', 'b'], 'v': np.ones(15)} ) Note that there's only two unique IDs, so a simple groupby('id') won't work. Also, the IDs don't alternate/repeat in a regular manner. What I came up with was to recreate the

问题 Given a DataFrame with an ID column and corresponding values column, how can I aggregate (let's say sum) the values within blocks of repeating IDs? Example DF: import numpy as np import pandas as pd df = pd.DataFrame( {'id': ['a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'a', 'a', 'b', 'a', 'b', 'b', 'b'], 'v': np.ones(15)} ) Note that there's only two unique IDs, so a simple groupby('id') won't work. Also, the IDs don't alternate/repeat in a regular manner. What I came up with was to recreate the

问题 I've got a few tables in a Postgres 12.3 database. The first one is named category : id|template_id|name |entry_count|is_base_template|can_rename|can_delete|section|userId|parentCategoryId| --|-----------|---------------|-----------|----------------|----------|----------|-------|------|----------------| 1| |Notes | 0|true |true |true |A | | | 2| |ToDo | 0|true |true |true |A | | | 3| 1|Notes | 0|false |true |true |A | 1| | 4| 2|ToDo | 0|false |true |true |A | 1| | 5| |Must Do | 0|false | | |A

问题 def stack_plot(data, xtick, col2='project_is_approved', col3='total'): ind = np.arange(data.shape[0]) plt.figure(figsize=(20,5)) p1 = plt.bar(ind, data[col3].values) p2 = plt.bar(ind, data[col2].values) plt.ylabel('Projects') plt.title('Number of projects aproved vs rejected') plt.xticks(ind, list(data[xtick].values)) plt.legend((p1[0], p2[0]), ('total', 'accepted')) plt.show() def univariate_barplots(data, col1, col2='project_is_approved', top=False): # Count number of zeros in dataframe

问题 I have a dataframe with a column ID as an identifier and some other columns of different types (factors and numerics). It looks like this df <- data.frame(id = c(1, 1, 1, 1, 1, 2, 2, 3, 3, 4, 4, 4, 4), abst = c(0, NA, 2, NA, NA, NA, 0, 0, NA, 2, NA, 3, 4), farbe = as.factor(c("keine", NA, "keine", NA, NA, NA, "keine", "keine", NA, NA, NA, "rot", "rot")), gier = c(0, NA, 5, NA, NA, NA, 0, 0, NA, 1, NA, 6, 2)) Now I want to combine the duplicate IDs. The numeric columns are defined as the mean

问题 Say I have two entities Notifications and Users. I want to mark that a user has seen a specific notification. This would commonly be done with a many-to-many relationship e.g. UserNotification Because there is no invariant around this relationship (we don't care if "all" users have seen the notification) these users shouldn't be on the notification aggregate. On the opposite side, the users aggregate doesn't need a list of notifications on it So that leads to say that the UserNotification

问题 The bounty expires tomorrow . Answers to this question are eligible for a +50 reputation bounty. Bolle is looking for an up-to-date answer to this question. This question is building up on another question R combining duplicate rows by ID with different column types in a dataframe. I have a datatable with a column time and some other columns of different types (factors and numerics). Here is an example: dt <- data.table(time = c(1, 1, 1, 1, 1, 2, 2, 3, 3, 4, 4, 4, 4), abst = c(0, NA, 2, NA,

问题 I have a pandas data-frame: id city 000.tushar@gmail.com Bangalore 00078r@gmail.com Mumbai 0007ayan@gmail.com Jamshedpur 0007ayan@gmail.com Jamshedpur 000.tushar@gmail.com Bangalore 00078r@gmail.com Mumbai 00078r@gmail.com Vijayawada 00078r@gmail.com Vijayawada 00078r@gmail.com Vijayawada I want to find id-wise the maximum occurring city name. So that for a given id I can tell that - this is his favorite city: id city 000.tushar@gmail.com Bangalore 00078r@gmail.com Vijayawada 0007ayan@gmail