I am making a portfolio tracker in Google Sheets and wanted to know if there is a way to link the "TICKER" column with the code in the "PRICE" column that is used to pull JSON data from Coin Gecko. I was wondering if there was an f-string like there is in Python where you can insert a variable into the string itself. Ergo, every time the Ticker column is updated the coin id will be updated within the API request string. Essentially, string interpolation
For example:
TICKER PRICE
BTC =importJSON("https://api.coingecko.com/api/v3/coins/markets?vs_currency=usd&ids={BTC}","0.current_price")
You could use CONCATENATE for this:
https://support.google.com/docs/answer/3094123?hl=en
CONCATENATE function
Appends strings to one another.
Sample Usage
CONCATENATE("Welcome", " ", "to", " ", "Sheets!")
CONCATENATE(A1,A2,A3)
CONCATENATE(A2:B7)
Syntax
CONCATENATE(string1, [string2, ...])
string1 - The initial string.
string2 ... - [ OPTIONAL ] - Additional strings to append in sequence.
Notes
When a range with both width and height greater than 1 is specified, cell values are appended across rows rather than down columns. That is, CONCATENATE(A2:B7) is equivalent to CONCATENATE(A2,B2,A3,B3, ... , A7,B7).
See Also
SPLIT: Divides text around a specified character or string, and puts each fragment into a separate cell in the row.
JOIN: Concatenates the elements of one or more one-dimensional arrays using a specified delimiter.
Related
Hello,
I am analyzing the next dataset with this information .
The column ['program_number'] is an object but I want to change it to a integer colum.
I have tried to replace some values but it doesn´t work.
as you can see, some values like 6 is duplicate. like '6 ' and 6.
How can I resolve it? Many thanks
UPDATE
Didn't see 1X and 3X at first.
If you need those numbers and just want to remove the X then:
df["Program"] = df["Program"].str.strip(" X").astype(int)
If there is data in the column which aren't numbers or which shouldn't be converted, you can use pd.to_numeric with errors='corece'. If there are cells which can't be converted, you'll get NaN. Be aware that this will result in floating numbers.
df["Program"] = pd.to_numeric(df["Program"], errors="coerce")
old
You want to use str.strip() here, rather than replace.
Try this:
df1['program_number'] = df1['program_number'].str.strip().astype(int)
I have a dataset like a multiple choice quiz result. One of the fields is semi-colon delimited. I would like to break these in to true/false columns.
Input
Student
Answers
Alice
B;C
Bob
A;B;D
Carol
A;D
Desired Output
Student
A
B
C
D
Alice
False
True
True
False
Bob
True
True
False
True
Carol
True
False
False
True
I've already tried "Split multi-valued cells" and "Split in to several columns", but these don't give me what I would like.
I'm aware that I could do a custom grel/python/jython along the lines of "if value in string: return true" for each value, but I was hoping there would be a more elegant solution.
Can anyone suggest a starting point?
GREL in OpenRefine has a somehow limited number of datastructures, but you can still build simple algorithms with it.
For your encoding you need two datastructures:
a list (technical array) of all available categories.
a list of the categories in the current cell.
With this you can check for each category, whether it is present in the current cell or not.
Assuming that the number of all available categories is somehow assessable,
I will use a hard coded list ["A", "B", "C", "D"].
The list of categories in the current cell we get via value.split(/\s*;\s*/).
Note that I am using an array instead of string matching
and use splitting with a regular expression considering whitespace.
This is mainly defensive programming and hopefully the algorithm will still be understandable.
So let's wrap this all together into a GREL expression and create a new column (or transform the current one):
with(
value.split(/\s*;\s*/),
cell_categories,
forEach(
["A", "B", "C", "D"],
category,
if(cell_categories.inArray(category), 1, 0)))
.join(";")
You can then split the new column into several columns using ; as separator.
The new column names you have to assign manually (sry ;).
Update: here is a more elaborate version to automatically extract the categories.
The idea is to create a single record for the whole dataset to be able to access all the entries in the column "Answers" and then extract all available categories from it.
Create a new column "Record" with content "Record".
Move the column "Record" to the beginning.
Blank down the column "Record".
Add a new column "Categories" based on the column "Answers" with the following GREL expression:
if(row.index>0, "",
row.record.cells["Answers"].value
.join(";")
.split(/\s*;\s*/)
.uniques()
.sort()
.join(";"))
Fill down the column "Categories".
Add a new column "Encoding" based on the column "Answers with the following GREL expression:
with(
value.split(/\s*;\s*/),
cell_categories,
forEach(
cells["Categories"].value.split(";"),
category,
if(cell_categories.inArray(category), 1, 0)))
.join(";")
Split the column "Encoding" on the character ;.
Delete the columns "Record" and "Categories".
I have a table with a nested json array in (columnname), made up of 5 parts (col1,col2,col3,col4,col5), with a number of "rows". col5 is the row number. I am trying to extract col3 for row 1.
A colleague of mine suggested I use element_at(columnname, 1), which returns the whole json string for that row of data, but I want to extract one part of that data. I cannot find how to extract one part of that json string from what I have.
Is there a way to extract col3?
Found it. element_at(columnname,1).col1
I have been trying to remove the exponential in a string for the longest time to no avail.
The column involves strings with alphabets in it and also long numbers of more than 24 digits. I tried converting the column to string with .astype(str) but it just reads the line as "1.234123E+23". An example of the table is
A
345223423dd234324
1.234123E+23
how do i get the table to show the full string of digits in pandas?
b = "1.234123E+23"
str(int(float(b)))
output is '123412299999999992791040'
no idea how to do it in pandas with mixed data type in column
I have a pandas data frame which doesn't have an index yet (just artificial 1,2,3,.. index)
Column 'store', 'style' is string, column 'color', 'size' is a long int.
None of them are unique by themselves, but the concatenation of them are unique.
I want to concatenate them to produce an index, but
df2['store']+df2['style']+str(df2['color'])+str(df2['size'])
or
df2['store']+df2['style']+df2['color'].to_string()+df2['size'].to_string()
both doesn't work. I think it takes the whole column, force it to become a string and concatenate which results in weird symbols. And merges doesn't work correctly.
What's the correct way to concatenate a string column and a long column?
This should be:
df2['store'] + df2['style'] + df2['color'].astype(str) + df2['size'].astype(str)
Explanation: str(df2['size']) will make a string representation of the full column (one string, comparable as to what you see if you print the string), while .astype(str) will convert all values of the series to strings.
to_string gives the same result as str() (but takes optional parameters to control the result)