How to count the number of elements in a list and create a new column?

Related searches

I have a df as follows:

   Col1                                           Col2
    0                                     [7306914, 7306915]
    1                                     [7295911, 7295912]
    2                                              [7324496]
    3                                     [7294109, 7294110]
    4                                              [7313713]

The second column is a list. what I would like is to create a new column that contains the total number of elements in the list

Expected Output:

   Col1        Col2           Col3
    0    [7306914, 7306915]      2
    1    [7295911, 7295912]      2
    2    [7324496]               1
    3    [7294109, 7294110]      2
    4    [7313713]               1

Use Series.str.len. This is a vectorized method and is more efficient than apply function, which is essentially looping under-the-hood:

df = pd.DataFrame([{'Col1': 0, 'Col2': [7306914, 7306915]}, {'Col1': 1, 'Col2': [7295911, 7295912]}, {'Col1': 2, 'Col2': [7324496]}, {'Col1': 3, 'Col2': [7294109, 7294110]}, {'Col1': 4, 'Col2': [7313713]}])

df['Col3'] = df['Col2'].str.len()

[out]

print(df)

   Col1                Col2  Col3
0     0  [7306914, 7306915]     2
1     1  [7295911, 7295912]     2
2     2           [7324496]     1
3     3  [7294109, 7294110]     2
4     4           [7313713]     1

Getting frequency counts of a columns in Pandas DataFrame , This method can be used to count frequencies of objects over single columns. After grouping a DataFrame object on one column, we can apply� Use Advanced Filter to create a list of the unique entries in the appropriate column. Use the =Countif function to count the number of times each unique entry appears in the original list. Sort,

Try this:

df_tmp = pd.DataFrame({'col1':[[1,2,3], [1,2]]}).reset_index()
In [360]:

df_tmp.head()
Out[360]:
    index   col1
0   0       [1, 2, 3]
1   1       [1, 2]
In [364]:

df_tmp['len'] = df_tmp.apply(lambda x: len(x['col1']), axis=1)
In [365]:

df_tmp
Out[365]:
    index   col1        len
0   0       [1, 2, 3]   3
1   1       [1, 2]      2

Apply should be most faster way for that.

Use pandas.DataFrame.count to count the number of elements , Apply a conditional to each element of a column by applying the conditional to the entire DataFrame column. Use the previous result to generate a subset of the � Build a new list of sizes of internal lists. Then pass the list to sum () to get total number of elements in list of lists i.e. # List of lists. listOfElems2D = [ [1,2,3,45,6,7], [22,33,44,55], [11,13,14,15] ] # Get the size of list of list using list comprehension & sum () function.

Using the DataFrame.apply() or DataFrame.apply() like this:

df['Col3'] = df['Col2'].apply(len)

Hope it could help you.

Counting Values & Basic Plotting in Python, Count the number of times a value occurs using .values_count(); Plot bar Nested inside this list is a DataFrame containing the results generated by the Since you'll be using pandas methods and objects, import the pandas library. add a new cell for every step; add to the same cell and re-run it to inspect the output. If there is not an empty string, increment that column's count; For the column counts, you could just use another list. For example counts is our list, counts[0] will be the number of elements of the column "column", counts[1] will be the number of elements of the column "count", etc.

Deriving New Columns & Defining Python Functions, Make new columns from existing data and build custom functions. Nested inside this list is a DataFrame containing the results generated by the SQL query Since you'll be using pandas methods and objects, import the pandas library. Count the values in the platform column to get an idea of the distribution (for a quick� Python List count() The count() method returns the number of times the specified element appears in the list.

Ways to count values in a worksheet, You can quickly create a PivotTable by how the Fields correlate to the Fields list. Let's look at a sample scenario of a Sales spreadsheet, where you can count how many sales values are a table or range, then click New Worksheet, and then click OK. I would like to count the unique observations by a group in a pandas dataframe and create a new column that has the unique count. Importantly, I would not like to reduce the rows in the dataframe; effectively performing something similar to a window function in SQL.

To get a "new column" you could use transform: In [13]: df.groupby(["item", "color"])["id"].transform("count") Out[13]: 0 2 1 2 2 2 3 1 4 2 dtype: int64 I recommend reading the split-apply-combine section of the docs .

Comments