Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Some of the options and variables that have been removed in MySQL 8.0 have been listed below:innodb_available_undo_logs: It refers to the total number of InnoDB rollback segments. It is different from innodb_rollback_segments, which displays the number of active rollback segments. It was removed in MySQL 8.0.2.Qcache_free_blocks: It refers to the number of free memory blocks in query cache. It was removed in MySQL 8.0.3.Qcache_free_memory: It refers to the amount of free memory for the query cache. It was removed in MySQL 8.0.3.bootstrap: It is used by MySQL installation scripts. It was removed in MySQL 8.0.0.date_format: It is the DATE format (unused). It ... Read More
DocumentationThere are many sources for the documentation of MySQL, but the primary website is https://dev.mysql.com/doc/.The developers of MySQL have provided information about the new and upcoming features in the server in the website: MySQL Server BlogCommunity ResourceCommunity resource also play an important role. The forum is https://forums.mysql.com. There are many forums, and they have been grouped into many categories. Some of them have been listed below:MigrationMySQL UsageMySQL ConnectorsProgramming LanguagesTools3rd-Party ApplicationsStorage EnginesMySQL TechnologySQL StandardsBusinessTechnical SupportOracle also offers technical support as MySQL enterprise. Organizations that use MySQL DBMS for important, and business-critical production applications get a subscription that includes the following:MySQL Enterprise ... Read More
What is a bug?A bug is something that results in the program stalling or halting abruptly. This results in anomalies and causes complications, resulting in the task not getting complete. MySQL helps resolve these bugs, once they are reported.Some bugs have fixes since they would have been previously reported, and fixes would have been provided.Pre-requisitesBefore posting a bug report, it is important to verify that the bug hasn’t been reported already. For this purpose, look for the problem in the MySQL manual at https://dev.mysql.com/doc/. The manual is always updated with solutions to newly found issues.If there is a parsing error ... Read More
MySQL is an open source SQL (structured query language) database management system. It is a system that helps store and manage data efficiently. Database generally stores data in a structured fashion.Timeline of MySQLUnireg, which is the code base of MySQL, was started in 1981.MySQL was founded in 1995 in Sweden.In 2000, MySQL went open source, so it could be accessed and used by all.In the year 2001, Marten Mickos was elected as the CEO of MySQL.In the year 2002, MySQL launched its headquarters in USA, in addition to Sweden headquarters.In 2003, MySQL entered into a partnership with SAP, and many features ... Read More
MySQL is an open source SQL (structured query language) database management system. Let us see some of its characteristics:ConsistentMySQL server is quick, and reliable. It stores data efficiently in the memory ensuring that data is consistent, and not redundant.ScalableMySQL server is scalable and easy to use. Scalability refers to the ability of systems to work easily with small amounts of data, large amounts of data, clusters of machines, and so on. It is also used in production environment due to its scalability and ease of use.Databases over InternetIt provides high security, improved connectivity, and speed thereby making it suitable to ... Read More
Input −Assume you have a DataFrame, and the result for shifting the first column and fill the missing values are, one two three 0 1 10 100 1 2 20 200 2 3 30 300 enter the value 15 one two three 0 15 1 10 1 15 2 20 2 15 3 30SolutionTo solve this, we will follow the below approach.Define a DataFrameShift the first column using below code, data.shift(periods=1, axis=1)Get the value from user and verify if it is divisible by 3 and 5. If the result is true then fill missing ... Read More
Assume you have a dataframe with time series data and the result for truncated data is, before truncate: Id time_series 0 1 2020-01-05 1 2 2020-01-12 2 3 2020-01-19 3 4 2020-01-26 4 5 2020-02-02 5 6 2020-02-09 6 7 2020-02-16 7 8 2020-02-23 8 9 2020-03-01 9 10 2020-03-08 after truncate: Id time_series 1 2 2020-01-12SolutionTo solve this, we will follow the steps given below −Define a dataframe.Create date_range function inside start=’01/01/2020’, periods = 10 and assign freq = ‘W’. It will generate ten dates from given start date to next weekly start dates and store it as df[‘time_series’].df['time_series'] ... Read More
Assume, you have series and the result for autocorrelation with lag 2 is, Series is: 0 2.0 1 10.0 2 3.0 3 4.0 4 9.0 5 10.0 6 2.0 7 NaN 8 3.0 dtype: float64 series correlation: -0.4711538461538461 series correlation with lags: -0.2933396642805515SolutionTo solve this, we will follow the steps given below −Define a seriesFind the series autocorrelation using the below method, series.autocorr()Calculate the autocorrelation with lag=2 as follows, series.autocorr(lag=2)ExampleLet’s see the below code to get a better understanding, import pandas as pd import numpy as np series = ... Read More
Assume you have a dataframe and the result for exporting into pickle file and read the contents from file as, Export to pickle file: Read contents from pickle file: Fruits City 0 Apple Shimla 1 Orange Sydney 2 Mango Lucknow 3 Kiwi WellingtonSolutionTo solve this, we will follow the steps given below −Define a dataframe.Export the dataframe to pickle format and name it as ‘pandas.pickle’, df.to_pickle('pandas.pickle')Read the contents from ‘pandas.pickle’ file and store it as result, result = pd.read_pickle('pandas.pickle')ExampleLet’s see the below implementation to get better understanding, import pandas as pd df = pd.DataFrame({'Fruits': ... Read More
Assume you have the following sample json data stored in a file as pandas_sample.json{ "employee": { "name": "emp1", "salary": 50000, "age": 31 } }The result for after converting to csv as, , employee age, 31 name, emp1 salary, 50000SolutionTo solve this, we will follow the steps given below −Create pandas_sample.json file and store the JSON data.Read json data from the file and store it as data.data = pd.read_json('pandas_sample.json')Convert the data into dataframedf = pd.DataFrame(data)Apple df.to_csv function to convert the data as csv file format, df.to_csv('pandas_json.csv')ExampleLet’s see the below implementation ... Read More