Python / Pandas-计算具有特定索引的行数

Question

I have this dataframe:

     content
id         
17         B
17         A
 6         A
15         A
...

I want to count how many rows have the index 17 (in this case that would be 2). Is there a way to do that?

Answer 1

You can groupby level

df.groupby(level=0).count()

Or reset_index()

df.reset_index().groupby('id').count()

Answer 2

You can try:

sum(df.index == 17)

df.index == 17 returns an array with boolean with True when index value matches else False . And while using sum function True is equivalent to 1 .

Answer 3

Problem: How to count the quantity of index label?

Input: # Your DataFrame
       test_dict = {'id': ['17', '17', '6', '15'], 'content': ['B', 'A', 'A', 'A']}
       testd_df = pd.DataFrame.from_dict(test_dict) # create DataFrame from dict
       testd_df.set_index('id', inplace=True) # set 'id' as index in inplace way
       testd_df
Output: 
             |content
        --------------
         id  |
        -------------
         17  |      B
         17  |      A
          6  |      A
         15  |      A

Solution: Use api pandas.Index.value_counts

Based on the document, pandas.Index.value_counts will return object containing counts of unique values and return a pd.Series .

so now, I can select the specific index I want by using pandas.Series.loc (not get confused with .iloc )

# Solution
Input:  index_count = pd.Index(testd_df.index).value_counts() # count value of unique value
        index_count

Output: 17    2
        15    1
        6     1
        dtype: int64
---------------------------------
Input:  index_count.loc['17'] # select the information you care about
Output: 2