为熊猫中的每一行计算列中的特定值

Question

你好，我有一个数据文件，如：

Species COL1 COL2 COL3 COL4 COL5
SP1     0    0    0    1-2  0-1-2
SP2     1-2  2    0    1    0
SP3     0-1  1    2    0    1-2 

我想添加新的列来计算每一行的特定唯一值的数目，例如：

Species COL1 COL2 COL3 COL4 COL5  count_0 count_1-2 count_0-1-2 count_1 count_2 
SP1     0    0    0    1-2  0-1-2 3       1         1           0       0
SP2     1-2  2    0    1    0     2       1         0           1       1
SP3     0-1  1    2    0    1-2   1       1         0           2       1

有人知道吗？

Answer 1

您可以使用熊猫图书馆中的熊猫图书馆方法来计算数据中每一行中每个唯一值的出现次数。

# Loop through each row of the dataframe
for index, row in df.iterrows():
    # Create a series object for the current row
    series = pd.Series(row)

    # Count the number of occurrences of each unique value in the row
    counts = series.value_counts()

    # Add the count values to the current row of the dataframe
    df.loc[index, 'count_0'] = counts[0] if 0 in counts else 0
    df.loc[index, 'count_1-2'] = counts['1-2'] if '1-2' in counts else 0
    df.loc[index, 'count_0-1-2'] = counts['0-1-2'] if '0-1-2' in counts else 0
    df.loc[index, 'count_1'] = counts[1] if 1 in counts else 0
    df.loc[index, 'count_2'] = counts[2] if 2 in counts else 0

Answer 2

示例

data = {'Species': {0: 'SP1', 1: 'SP2', 2: 'SP3'},
        'COL1': {0: '0', 1: '1-2', 2: '0-1'},
        'COL2': {0: '0', 1: '2', 2: '1'},
        'COL3': {0: '0', 1: '0', 2: '2'},
        'COL4': {0: '1-2', 1: '1', 2: '0'},
        'COL5': {0: '0-1-2', 1: '0', 2: '1-2'}}
df = pd.DataFrame(data)

码

df1 = (df.set_index('Species').apply(lambda x: x.value_counts(), axis=1)
       .add_prefix('count_').fillna(0).astype('int'))

df1

         count_0    count_0-1   count_0-1-2 count_1 count_1-2   count_2
Species                     
SP1      3          0            1          0       1           0
SP2      2          0            0          1       1           1
SP3      1          1            0          1       1           1

使期望的输出

concat & df1

pd.concat([df.set_index('Species'), df1], axis=1)