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Questions - multiple-columns

Update handle if else condition

I have the below scenario to be handled : If a is not present in dynamodb, then update a with received value and not b. If a is present in dynamodb, then, update b with received value and not a. Can...
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ashritha balu

if-statement

amazon-dynamodb

conditional-statements

multiple-columns

Votes: 0

Answers: 1

Latest Answer

you can use Condition Expressions in dynamoDB to manipulate data . for more help there is below link of documents you can use Amazon Documentation it will help you more than any documents : Reference:...
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Rockinroll

multiply number of rows that have specefic value in a column

I have data that contain some rows and columns like this for example col1 col2 col3 col4 r1 A 1 1 2 r2 B 3 1 2 r3 A 4 1 2 r4 D 10...
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Reda

r

dataframe

row

multiple-columns

Votes: 0

Answers: 3

Latest Answer

1) dplyr We can use across in dplyr or replace the first argument to across with where(is.numeric) : library(dplyr) DF %>% mutate(across(-col1, ~ ifelse(col1 %in% c("A", "B"), ...
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G. Grothendieck

CSS Fixed column width before wrap and full width after wrap

What I need Look at the image for a general idea of what I need. I am avoiding media queries as I have a sidebar on my page which pushes all the main content, which then makes @media queries terrible...
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Magnus Enebakk

html

css

flexbox

grid

multiple-columns

Votes: 0

Answers: 1

Latest Answer

There is a scenerio that you can do that we make our .container max-width:1000px; than we gave .red flex-grow:3 .blue flex-grow:7 So when they split 1000px together .red will take 300px (flex-grow:3)...
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UPinar

select multiple separately columns by iloc in pandas

my file contains 15 columns and 10 rows. I want to select some columns between them like this (1:5 and 7:11 and 13:15). I wrote it similer to: 'df.iloc[: ,[1:6,7:12,13:]]' or 'df.iloc[: ,[[1:6],[7:12...
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vahid vhr

python

pandas

select

multiple-columns

rows

Votes: 0

Answers: 1

Latest Answer

You can use np.r_ to have slice notation: df = pd.DataFrame(columns=list('ABCDEFGHIJKLMNOPQRSTUVWXYZ')) df1 = df.iloc[:, np.r_[1:5, 7:11, 13:15]] print(df1) # Output Empty DataFrame Columns: [B, C, D...
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Corralien

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