Get First Entries in Rows of List

How to get first elements of each list in a list of lists

You just iterate as it were a flat list

lists = [[1, 2, 3, 5], [3, 4, 5, 6], [7, 8, 9, 3],[10, 11, 12, 13]]
 
for l in lists:
   if l != []:
     print(l[0])

How to get every first element in 2 dimensional list

You can get the index [0] from each element in a list comprehension

>>> [i[0] for i in a]
[4.0, 3.0, 3.5]

Get access to the first row of a list in R

 A=list()
 A[[1]]=c(1,2)
 A[[2]]=c(3,4)
 A[[3]]=c(5,6)

 A
 # [[1]]
 # [1] 1 2

 # [[2]]
 # [1] 3 4

 # [[3]]
 # [1] 5 6

I. First Solution using sapply() function for just first elements

 sapply(A,'[[',1)
 # [1] 1 3 5

For Getting lets say 1st, 3rd, 4th elements of each nested list. Not applicable in this example

 sapply(A,`[`,c(1,3,4))

II. Second Solution using for loop

 for(i in 1:length(A)){
 print (A[[i]][1])
 }
 # [1] 1
 # [1] 3
 # [1] 5

Accessing every 1st element of Pandas DataFrame column containing lists

You can use map and a lambda function

df.loc[:, 'new_col'] = df.A.map(lambda x: x[0])

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Python: Fetch first 10 results from a list

list[:10]

will give you the first 10 elements of this list using slicing.

However, note, it's best not to use list as a variable identifier as it's already used by Python: list()

To find out more about these type of operations you might find this tutorial on lists helpful and the link @DarenThomas provided Explain Python's slice notation - thanks Daren)

Getting the first element from each list in a column of lists

The easiest way is using str.get

# df['new_column'] = df['codes'].str.get(0)
df['new_column'] = df['codes'].str[0]

However, I would suggest a list comprehension for speed, if there are no NaNs:

df['new_column'] = [l[0] for l in df['codes']]

If lists can be empty, you can do something like:

df['new_column'] = [l[0] if len(l) > 0 else np.nan for l in df['codes']]

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To handle NaNs with the list comprehension, you can use loc to subset and assign back.

m = df['codes'].notna()
df.loc[m, 'new_column'] = [
    l[0] if len(l) > 0 else np.nan for l in df.loc[m, 'codes']]

Obligatory why-is-list-comp-worth-it link: For loops with pandas - When should I care?

Selecting all rows with the first 5 different values of a list

You can use np.unique to get the unique values of a list:

first_vals = np.unique(list)[:5]

new_array = []
for index, value in enumerate(list):
    if value in first_vals:
        new_array.append([index, value])
print(new_array)

Output:

[[0, 0], [1, 0], [2, 1], [3, 0], [4, 2], [5, 1], [6, 3], [10, 4], [11, 4], [12, 1], [14, 3], [15, 1], [16, 3], [17, 2], [18, 3]]

(Note: it's bad practice use names of Python builtins as variable names, e.g. list

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