indexing

is primary key automatically indexed in postgresql? [closed]

╄→гoц情女王★ 提交于 2021-01-21 12:09:51
问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 3 months ago . Improve this question I have created table name as d with ID column as primary key and then just inserted records as shown in output, but after fetching all records this output still displayed same as order in which records are inserted. but output as a see now not in ordered form. 回答1: PostgreSQL

Finding the index of the first element (e.g “True”) from a series/column

孤街醉人 提交于 2021-01-21 07:13:51
问题 How do I find the index of an element (e.g "True") in a series or a column? For example I have a column, where I want to identify the first instance where an event occur. So I write it as Variable = df["Force"] < event This then creates a boolen series of Data where it is False, until the first instance it becomes True. How then do I find the index of data point? Is there are better way? 回答1: Use idxmax to find the first instance of the maximum value. In this case, True is the maximum value.

Access last elements of inner multiindex level in pandas dataframe

可紊 提交于 2021-01-21 03:51:40
问题 In a multi index pandas dataframe I want to access the last element of the second index for all values of the first index. The number of levels in the second index vary depending on the value of the first index. I went through the pandas multi index documentation but could not find anything that does that. For example, for the data frame below: arrays = [ ['bar', 'bar', 'baz', 'foo', 'foo', 'foo', 'qux'], ['one', 'two', 'one', 'one', 'two', 'three', 'one']] tuples = list(zip(*arrays)) index =

Access last elements of inner multiindex level in pandas dataframe

喜你入骨 提交于 2021-01-21 03:51:03
问题 In a multi index pandas dataframe I want to access the last element of the second index for all values of the first index. The number of levels in the second index vary depending on the value of the first index. I went through the pandas multi index documentation but could not find anything that does that. For example, for the data frame below: arrays = [ ['bar', 'bar', 'baz', 'foo', 'foo', 'foo', 'qux'], ['one', 'two', 'one', 'one', 'two', 'three', 'one']] tuples = list(zip(*arrays)) index =

how to get top 3 values in php array and their index

你离开我真会死。 提交于 2021-01-20 04:28:26
问题 I want to get the highest value, the second highest value and the third highest value For example, I have an array like: $n = array(100,90,150,200,199,155,15,186); I know the method to get the max value and its index: echo max($n); //200 $maxs = array_keys($n, max($n)); echo $maxs[0]; //3 I want to get the top 3 values and their index like : value: 200, 199, 186 index:3,4,7 How can i get them? 回答1: Try this: $n = array(100,90,150,200,199,155,15,186); rsort($n); $top3 = array_slice($n, 0, 3);

Flatten a single-leveled multiIndex

两盒软妹~` 提交于 2021-01-07 02:21:52
问题 Indexing into a Pandas DataFrame throws an error when: The query has multiple copies of the same value. The index in the DataFrame is a single-level MultiIndex. The below code shows a simple example import pandas as pd # columns as str class -- works D = pd.DataFrame([[1,2]], columns = ['A','B'], index = ['R1']) print(D.loc[:,['A','A']], '\n') # OK # columns as Index class -- works D = pd.DataFrame([[1,2]], columns = pd.Index(['A','B']), index = ['R1']) print(D.loc[:,['A','A']], '\n') # OK #

Flatten a single-leveled multiIndex

自作多情 提交于 2021-01-07 02:21:28
问题 Indexing into a Pandas DataFrame throws an error when: The query has multiple copies of the same value. The index in the DataFrame is a single-level MultiIndex. The below code shows a simple example import pandas as pd # columns as str class -- works D = pd.DataFrame([[1,2]], columns = ['A','B'], index = ['R1']) print(D.loc[:,['A','A']], '\n') # OK # columns as Index class -- works D = pd.DataFrame([[1,2]], columns = pd.Index(['A','B']), index = ['R1']) print(D.loc[:,['A','A']], '\n') # OK #

Flatten a single-leveled multiIndex

橙三吉。 提交于 2021-01-07 02:21:16
问题 Indexing into a Pandas DataFrame throws an error when: The query has multiple copies of the same value. The index in the DataFrame is a single-level MultiIndex. The below code shows a simple example import pandas as pd # columns as str class -- works D = pd.DataFrame([[1,2]], columns = ['A','B'], index = ['R1']) print(D.loc[:,['A','A']], '\n') # OK # columns as Index class -- works D = pd.DataFrame([[1,2]], columns = pd.Index(['A','B']), index = ['R1']) print(D.loc[:,['A','A']], '\n') # OK #

Slow PostgreSQL query in production - help me understand this explain analyze output

若如初见. 提交于 2020-12-29 04:30:24
问题 I have a query that is taking 9 minutes to run on PostgreSQL 9.0.0 on x86_64-unknown-linux-gnu, compiled by GCC gcc (GCC) 4.1.2 20080704 (Red Hat 4.1.2-46), 64-bit This query is automatically generated by hibernate for my application. It's trying to find all of the "teacher members" in a school. A membership is a user with a role in a group. There are several types of groups, but here what matters are schools and services. If someone is a teacher member in a service and a member in this

Slow PostgreSQL query in production - help me understand this explain analyze output

青春壹個敷衍的年華 提交于 2020-12-29 04:27:28
问题 I have a query that is taking 9 minutes to run on PostgreSQL 9.0.0 on x86_64-unknown-linux-gnu, compiled by GCC gcc (GCC) 4.1.2 20080704 (Red Hat 4.1.2-46), 64-bit This query is automatically generated by hibernate for my application. It's trying to find all of the "teacher members" in a school. A membership is a user with a role in a group. There are several types of groups, but here what matters are schools and services. If someone is a teacher member in a service and a member in this