i have a dataframe, that has a column 'A1' that contains multiple 'Hello' strings, postive as well as negative integers. I want to count the 'Hello' strings, all number >= 0 and all numbers < 0, so that i get three sums in the end.

So the Output should be for posNums 1, negNums 1 and helloCount 2

posNums = df.where(df['A1'] >= 0).sum()

This doesnt work obviously, because one cant compare string to int. But how can I add here some condition that skips the str when I count ints and vice versa?

CodePudding user response：

One way is to use pd.to_numeric:

import pandas as pd

df = pd.DataFrame({"A1": ["Hello", 1, -1, "Hello", "Hello", -2, 2, -3]})

agg_funcs = {
    "negative": lambda x: x.lt(0).sum(),
    "positive": lambda x: x.ge(0).sum(),
    "nans":     lambda x: x.isna().sum()
}

out = pd.to_numeric(df["A1"], errors="coerce").agg(agg_funcs)

out:

negative    3
positive    2
nans        3
Name: A1, dtype: int64

CodePudding user response：

Something like this you're looking for?

df = pd.DataFrame({'A1': ['hello', 1, 2, 3, 4, 5, -1, -2, -3, -4, -5, 'world']})
count_pos = df[df['A1'].apply(lambda x: isinstance(x, int) and x > 0)].count()
count_neg = df[df['A1'].apply(lambda x: isinstance(x, int) and x < 0)].count()
count_str = df[df['A1'].apply(lambda x: isinstance(x, str))].count()

Will output:
A1    5
dtype: int64
A1    5
dtype: int64
A1    2
dtype: int64