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1. Top results related to which province in indonesia provides data in csv format%3f

   • In which charset the data will be stored in database

     Top Answer

     Answered Jan 11, 2018 · 2 votes

     My database is in latin1 and collation is latin1_swedish_ci.

     Those are default settings. After you've created columns and tables, each column has a character set and a collation. Changing the defaults does not change existing column and table definitions.

     in my php file I'm using iso-8859-1.

     That is ok. Latin1 === iso-8859-1

     But If I store some characters like 'é' and some others I'm having problem in dowloading the content.

     I guess you mean the two-step process of retrieving the content from your table and then sending it to a web browser for rendering. It's the second step that might be wrong. Try setting this in the head section in your php / html file.

     <meta http-equiv="Content-Type" content="text/html;charset=iso-8859-1">-

     If you set that and everything renders correctly, you're done. You can also set it to unicode and see what happens. If you don't set it, browsers try to guess, using arcane rules.

     So we have added the default character-set to utf8 and skip-character-set-client-handshake in my.cnf file.

     Again, that doesn't change your existing columns in your tables.

     On what basis the data is being stored in database?

     Again, according to the character set chosen for each column.

     Sometimes its stored in latin1 format and sometimes in utf8.

     It's true.

     Best practice: mention the character set and collation for each table as you create it. If you have exceptions for some columns, mention them for the columns as you create them

     Best practice: for new databases use utfmb4.

     Best practice: always set your database connection character set.

     Best practice: read about how to create and manipulate unicode strings in php.

     Best practice: when standing up a new MySQL server, set the server-wide defaults to utfmb4 and utfmb4_general_ci.

     Unfortunately, migrating an existing php application to unicode can be a pain in the neck.

     Check all answers on Stack Overflow

     1/5

   • In JMeter how do I set multiple dynamic variables in User Parameters or CSV Data Set Config

     Top Answer

     Answered Nov 14, 2018 · 0 votes

     If you want to store the accessToken value into a JMeter property for using in other Thread Group(s) be aware that properties are global for the whole JVM and remain until JMeter is restarted so if you define a single accessToken property - each JMeter Thread (or iteration) will overwrite the value.

     The solution is to use current thread (or iteration) number as prefix or postfix, this can be done using either __${__threadNum} function or relevant JMeter Variable depending on how iteration is defined or both.

     Example setup:

     Demo:

     If you want to save the values into a file writing into the current one is not the best idea as you can (and most probably will) get malformed file due to a form of a race condition. So I would recommend using Sample Variables property instead.

     If you add the next line to user.properties file:

     sample_variables=email,password,accessToken

     JMeter will store the variables named ${email}, ${password}, and ${accessToken} along with their values in the .jtl results file which is basically CSV file which can be re-used anywhere else.

     However if you have a requirement to store only the credentials and the token you can go for the Flexible File Writer plugin and configure it to save the aforementioned variables values into a separate file, the relevant configuration would be as simple as:

     variable#0,variable#1,variable#2-

     You can install Flexible File Writer plugin using JMeter Plugins Manager

     Check all answers on Stack Overflow

     2/5

   • In R How do I convert this CSV data to XTS

     Top Answer

     Answered Jun 29, 2013 · 6 votes

     It is better to use read.zoo to read directly your ts in a zoo object, easily coerced to xts one:

     library(xts)ts.z <- read.zoo(text='2012.10.30,20:00,1.29610,1.29639,1.29607,1.29619,2952012.10.30,20:15,1.29622,1.29639,1.29587,1.29589,5692012.10.30,20:30,1.29590,1.29605,1.29545,1.29574,4512012.10.30,20:45,1.29576,1.29657,1.29576,1.29643,5222012.10.30,21:00,1.29643,1.29645,1.29581,1.29621,5262012.10.30,21:15,1.29621,1.29644,1.29599,1.29642,330', sep=',',index=1:2,tz='',format="%Y.%m.%d %H:%M")as.xts(ts.z) V3 V4 V5 V6 V72012-10-30 20:00:00 1.29610 1.29639 1.29607 1.29619 2952012-10-30 20:15:00 1.29622 1.29639 1.29587 1.29589 5692012-10-30 20:30:00 1.29590 1.29605 1.29545 1.29574 4512012-10-30 20:45:00 1.29576 1.29657 1.29576 1.29643 5222012-10-30 21:00:00 1.29643 1.29645 1.29581 1.29621 5262012-10-30 21:15:00 1.29621 1.29644 1.29599 1.29642 330

     Check all answers on Stack Overflow

     3/5

   • Which one is best CSV or JSON in order to import big data (PHP)

     Top Answer

     Answered Oct 14, 2019 · 40 votes

     If you want to retrieve simple data as lists or rows with some columns the option #2 is the good one, you can read below a set of advantages and disadvantages:

     Pros

     • Less bandwidth needed because JSON needs more syntax characters to keep the format while CSV is as simple as use a character separator
     • Process data is faster because only needs to split by the separator character while JSON needs to interpret the syntax
     • Big data technology as Hadoop has an integrated parse for CSV format while needs a specific function for parse JSON (for example using Hive language).

     Cons

     • Unstructured data and more difficult to be read by humans
     • You have to take care as separator character cannot appear in data fields.

     If the data will contain complex data as tuples, arrays and structures JSON are better because:

     • Keeps a clear and structured format
     • Doesn't repeat data to reference it because one label could contain multiple data.

     Check all answers on Stack Overflow

     4/5

   • In readr()/read_csv, how to import data with all columns as character

     Top Answer

     Answered Apr 23, 2022 · 5 votes

     You can pick a default column type using:

     read_csv(readr_example("mtcars.csv"), col_types = cols(.default = col_character()))

     The cols specification will also let you define specific columns as well.

     Check all answers on Stack Overflow

     5/5

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2. stackoverflow.com › questions › 12877189python - float64 with pandas to_csv - Stack Overflow

   stackoverflow.com › questions › 12877189

   • Cached

   Nowadays there is the float_format argument available for pandas.DataFrame.to_csv and the float_precision argument available for pandas.from_csv. The original is still worth reading to get a better grasp on the problem. It was a bug in pandas, not only in "to_csv" function, but in "read_csv" too.

   Usage example

   df.to_csv('pandasfile.csv', float_format='%.3f')

   See more on stackoverflow

3. People also ask

   Is CSV a bug in pandas?

   • The original is still worth reading to get a better grasp on the problem. It was a bug in pandas, not only in "to_csv" function, but in "read_csv" too. It's not a general floating point issue, despite it's true that floating point arithmetic is a subject which demands some care from the programmer.

   python - float64 with pandas to_csv - Stack Overflow

   stackoverflow.com/questions/12877189/float64-with-pandas-to-csv

   See all results for this question

   How do I convert a CSV file to a floating point?

   • Inside your application, read the CSV file as usual and you will get those integer figures back. Then convert those values to floating point, dividing by the same factor you multiplied before. I have encountered this problem and this is the solution I have found. (I tried the other solution, but it didn't work correctly.)

   python - float64 with pandas to_csv - Stack Overflow

   stackoverflow.com/questions/12877189/float64-with-pandas-to-csv

   See all results for this question

   Can a CSV file contain all figures as integers?

   • If you desperately need to circumvent this problem, I recommend you create another CSV file which contains all figures as integers, for example multiplying by 100, 1000 or other factor which turns out to be convenient. Inside your application, read the CSV file as usual and you will get those integer figures back.

   python - float64 with pandas to_csv - Stack Overflow

   stackoverflow.com/questions/12877189/float64-with-pandas-to-csv

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4. www.geeksforgeeks.org › format-specifiers-in-javaFormat Specifiers in Java - GeeksforGeeks

   www.geeksforgeeks.org › format-specifiers-in-java

   • Cached

   Aug 17, 2022 · The general syntax of a format specifier is. % [flags] [width] [.precision] [argsize] typechar. The format () method of Formatter class accepts a wide variety of format specifiers. When an uppercase specifier is used, then letters are shown in uppercase. Otherwise, the upper- and lowercase specifiers perform the same conversion.

5. note.nkmk.me › python-pandas-to-csvpandasでCSVファイルの書き込み・追記（to_csv） | note.nkmk.me

   note.nkmk.me › python-pandas-to-csv

   • Cached

   • To_Csv()でcsvファイルに書き込み・保存（出力）
   • ヘッダー、インデックス出力の有無: 引数header, Index
   • 書き込みモード（新規作成・上書き・追記）: 引数mode
   • 欠損値nanの扱い: 引数na_Rep
   • 浮動小数点数floatの書式設定: 引数float_Format
   • 任意の書式に変換して保存

   pandas.DataFrameおよびpandas.Seriesのメソッドとしてto_csv()が用意されている。 第一引数にパスを指定すると、CSVファイルとして出力される。 パスは絶対パスかカレントディレクトリ（作業ディレクトリ）からの相対パスで指定する。カレントディレクトリの確認や変更については以下の記事を参照。 1. 関連記事: Pythonでカレントディレクトリを取得、変更（移動）

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   ヘッダー（列名、pandas.DataFrameのcolumns）、インデックス（行名、pandas.DataFrameのindex）の出力の有無は、引数header, indexにTrueかFalseで指定する。 デフォルトはどちらもTrueで、これまでの例のように、ヘッダーもインデックスも含めて出力される。

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   引数modeに書き込みモードを指定できる。組み込み関数open()と同じ値を指定可能。 1. 関連記事: Pythonでファイルの読み込み、書き込み（作成・追記） デフォルトはmode='w'。指定したパスが存在しない場合は新規作成、存在する場合は上書きとなる。 既存ファイルに上書きしてしまうのを防ぎたい場合はmode='x'とする。指定したパスが存在しない場合は新規作成、存在する場合はエラーとなり上書きされない。 追記はmode='a'。既存ファイルの末尾に新たな行として追記される。 一度保存したファイルに同じデータを追記する例を示す。header=Falseとしないとヘッダー（pandas.DataFrameのcolumns）もそのまま追記されるので注意。 mode='a'による追記は行...

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   欠損値NaNを含むpandas.DataFrameを例とする。 デフォルトでは欠損値は空白（空文字）として出力される。 引数na_repに欠損値を置き換える文字列を指定できる。 pandas.DataFrameの欠損値の処理については以下の記事を参照。 1. 関連記事: pandasで欠損値NaNを除外（削除）・置換（穴埋め）・抽出

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   以下のpandas.DataFrameを例とする。 print()での表示は指数表記となっているが、これはあくまでも表示設定によるもので、値自体が丸められているわけではない。 1. 関連記事: pandasの表示設定変更（小数点以下桁数、有効数字、最大行数・列数など） to_csv()で保存する場合、デフォルトでは値がそのまま保存される。 to_csv()の引数float_formatで、保存時の浮動小数点数floatの書式を指定できる。%によるprintf形式で用いる書式化文字列、または、format()などの呼び出し可能オブジェクトを指定する。 printf形式で指定する例。小数点以下の桁数を3桁にしている。 1. 組み込み型 - printf 形式の文字列書式化 — Python 3....

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   引数float_formatは名前の通りデータ型が浮動小数点数floatの列に対してのみ有効。整数intの列はそのまま。また、列ごとに異なる書式を指定することはできない。 整数intの列の書式を指定したい場合や、浮動小数点数floatに対して列ごとに異なる書式を指定したい場合は、元のpandas.DataFrameを任意の書式の文字列に変換してから保存する。 この例のように、整数intを16進数で保存した場合、pd.read_csv()では文字列として読み込まれるので注意。数値として扱いたい場合は読み込み後に変換する必要がある。 1. 関連記事: pandasで文字列と数値を相互変換、書式変更

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