[英]How to count the occurances of elements in list in for a row in pandas
I have a df that looks like this.我有一个看起来像这样的 df。 it is a multi-index df resulting from a group-by它是由 group-by 产生的多索引 df
grouped = df.groupby(['chromosome', 'start_pos', 'end_pos',
'observed']).agg(lambda x: x.tolist())
reference zygosity
chromosome start_pos end_pos observed
chr1 69428 69428 G [T, T] [hom, hom]
69511 69511 G [A, A] [hom, hom]
762273 762273 A [G, G, G] [hom, het, hom]
762589 762589 C [G] [hom]
762592 762592 G [C] [het]
For each row i want to count the number of het and hom in the zygosity.对于每一行,我想计算合子中 het 和 hom 的数量。 and make a new column called 'count_hom' and 'count_het'并创建一个名为“count_hom”和“count_het”的新列
I have tried using for loop it is slow and not very reliable with changing data.我试过使用 for 循环,它很慢,而且随着数据的变化不太可靠。 Is there a way to do this using something like df.zygosity.len().sum() but only for het or only for hom有没有办法使用 df.zygosity.len().sum() 之类的方法来做到这一点,但仅适用于 het 或仅适用于 hom
Use Series.apply
withList count
:将Series.apply
与List count
一起使用:
grouped['count_hom'] = grouped['zygosity'].apply(lambda x: x.count('hom'))
grouped['count_het'] = grouped['zygosity'].apply(lambda x: x.count('het'))
Instead of working on groupby result, you could adjust your groupby
construction a bit by including a lambda to agg
that counts "het" and "hom" values for each group at the time you build grouped
:您可以通过将 lambda 包含在agg
中来稍微调整groupby
的结构,而不是处理 groupby 结果,在您构建grouped
时计算每个组的“het”和“hom”值:
grouped = (df.groupby(['chromosome', 'start_pos', 'end_pos','observed'])
.agg(reference=('reference', list),
zygosity=('zygosity', list),
count_het=('zygosity', lambda x: x.eq('het').sum()),
count_hom=('zygosity', lambda x: x.eq('hom').sum())))
If you want to create a list out of all lists, you could use the following:如果要从所有列表中创建一个列表,可以使用以下命令:
cols = ['chromosome', 'start_pos', 'end_pos','observed']
out = df.groupby(cols).agg(**{c: (c, list) for c in df.columns.drop('reference')},
count_het=('zygosity', lambda x: x.eq('het').sum()),
count_hom=('zygosity', lambda x: x.eq('hom').sum()))
You can dynamically count all possible values using explode
+ groupby
, then value_counts
, then unstack
:您可以使用explode
+ groupby
动态计算所有可能的值,然后是value_counts
,然后是unstack
:
new_df = pd.concat([df, df['zygosity'].explode().groupby(level=[0,1,2,3]).value_counts().unstack(level=4).fillna(0).add_prefix('count_').astype(int)], axis=1)
Output: Output:
>>> new_df
reference zygosity count_het count_hom
chromosome start_pos end_pos observed
chr1 69428 69428 G [T, T] [hom, hom] 0 2
69511 69511 G [A, A] [hom, hom] 0 2
762273 762273 A [G, G, G] [hom, het, hom] 1 2
762589 762589 C [G] [hom] 0 1
762592 762592 G [C] [het] 1 0
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