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Python：如何使用函数过滤pandas.Series而不会丢失索引关联？

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我有一个 pandas.DataFrame ，我现在就在这里'm iterating over the rows. On each row I need to filter out some non valuable values and keep the indexes association. This is where I'：

for i,row in df.iterrows():
    my_values = row["first_interesting_column":]
    # here I need to filter 'my_values' Series based on a function
    # what I'm doin right now is use the built-in python filter function, but what I get back is a list with no indexes anymore
    my_valuable_values = filter(lambda x: x != "-", my_values)

我怎样才能做到这一点？

python filter pandas series

2 回答

  • 1

    我被IRC上的一个人建议了答案 . 这里是：

    w = my_values != "-" # creates a Series with a map of the stuff to be included/exluded
my_valuable_values = my_values[w]

    ......也可以缩短......

    my_valuable_values = my_values[my_values != "-"]

    ......当然，要避免再迈一步......

    row["first_interesting_column":][row["first_interesting_column":] != "-"]
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  • 0

    迭代行通常是不好的做法（并且非常慢） . 正如@JohnE建议你想使用applymap .

    如果我理解你的问题，我想你想做的是：

    import pandas as pd
from io import StringIO

datastring = StringIO("""\
2009    2010    2011   2012
1       4       -      4
3       -       2      3
4       -       8      7
""")
df = pd.read_table(datastring, sep='\s\s+')
a = df[df.applymap(lambda x: x != '-')].astype(np.float).values
a[~np.isnan(a)]
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