data:image/png;base64,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 all occurrences of a letter or a sequence of letters in a text with another or none.de:ersetze jedes _ durch _ in _
tshtower topTakes a table (typically from a CSV data set) as input and reports a summary of the table grouped by the field in the specified column number. The remaining three inputs are used only if the field values are numbers, in which case they can be grouped into buckets (e.g., decades, centuries, etc.). Those three inputs specify the smallest and largest values of interest and, most importantly, the width of a bucket (10 for decades, 100 for centuries). If the field isn't numeric, leave these three inputs empty or set them to zero. In that case, each string value of the field is its own bucket, and they appear sorted alphabetically.
The block reports a new table with three columns. The first column contains the bucket name or smallest number. The second column contains a nonnegative integer that says how many records in the input table fall into this bucket. The third column is a subtable containing the actual records from the original table that fall into the bucket.
If your buckets aren't of constant width, or you want to group by some function of more than one field, load the "Frequency Distribution Analysis" library instead. ca:gràfica de la taula _ agrupada pel camp: _ des de: _ fins: _ interval: _
1110add missing entries to a sorted list. Used for histogramspt:os itens de _ com a chave _ entre _ e _ com passo _ completados por _
ca:gràfica omplint _ amb clau: _ des de: _ fins: _ interval: _ amb _
11pt:desenha gráfico de barras de _ em (x: _ , y: _ ) com largura _ e altura _
ca:dibuixa gràfic _ a x: _ y: _ amplada: _ alçada: _
-200-100400200If smallest value < 0, the x axis isn't at the bottom.
Note that sort is by > not by <.
Not clear this can happen in a
histogram!max y1ratio1label delta y5010step2190draw x axis at y=00draw y axis902012i15Ready to draw bars.2data90ca:ordena taula _ pel camp _ en ordre _
ascending
descendingca:agrupa taula _ pel camp _ per intervals de _
de:animiere Setter _ Getter _ Delta _ über _ Sek. beschleunigt _
ca:modifica setter _ i getter _ amb delta _ en _ segons de forma _
pt:anima com modificador _ e inspector _ de _ em _ s de forma _
501de:Grad _
ca:graus de _ radians
pt:_ radianos em graus
0.5de:Fehler _
pt:assinala o erro _
de:gleite _ in _ Sek. beschleunigt: _
ca:llisca _ en _ segons de forma _
fr:glisser _ en _ sec. accélérer _
pt:desliza _ em _ s de forma _
501linearlinear
quadratic={
quadratic-in
quadratic-out
quadratic-in-out
}
cubic={
cubic-in
cubic-out
cubic-in-out
}
quart={
quart-in
quart-out
quart-in-out
}
sinusoidal={
sine-in
sine-out
sine-in-out
}
elasticde:animiere _ um _ in _ Sek. beschleunigt: _
ca:modifica _ amb _ en _ segons de forma _
pt:anima _ de _ em _ s de forma _
x positionsteps
x position
y position
direction
size
ghost effect
color effect
saturation effect
brightness effect
fisheye effect
whirl effect
pixelate effect
mosaic effect
negative effect
tempo
volume
balance501linearlinear
quadratic={
quadratic-in
quadratic-out
quadratic-in-out
}
cubic={
cubic-in
cubic-out
cubic-in-out
}
quart={
quart-in
quart-out
quart-in-out
}
sinusoidal={
sine-in
sine-out
sine-in-out
}
elasticdeltay positiondirectionsizetempovolumebalance112de:Beschleunigung _
ca:de forma _
pt:a forma _
linearlinear
quadratic={
quadratic-in
quadratic-out
quadratic-in-out
}
cubic={
cubic-in
cubic-out
cubic-in-out
}
quart={
quart-in
quart-out
quart-in-out
}
sinusoidal={
sine-in
sine-out
sine-in-out
}
elasticquadratic-outquadratic-inquadratic-in-outcubic-outcubic-incubic-in-outquart-outquart-inquart-in-outsine-outsine-insine-in-outelasticunknown easing function ""de:für _ = _ bis _ _ in _ Sek. beschleunigt: _ _
ca:per _ = _ fins _ _ en _ segons de forma _ _
pt:para _ de _ a _ _ em _ s de forma _ _
01001linearlinear
quadratic={
quadratic-in
quadratic-out
quadratic-in-out
}
cubic={
cubic-in
cubic-out
cubic-in-out
}
quart={
quart-in
quart-out
quart-in-out
}
sinusoidal={
sine-in
sine-out
sine-in-out
}
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