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Pandas: Find and print all floats in column


Pandas: Find and print all floats in column

By : KaRon Scott
Date : November 20 2020, 07:00 PM
Does that help So I assuming you have column type is object , usually pandas only have one data type per columns
code :
df.col_1.map(type)==float# will return bool


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possible bug: pandas trying to fill int column with floats

possible bug: pandas trying to fill int column with floats


By : SA SUFIAN
Date : March 29 2020, 07:55 AM
I hope this helps you . You are chain indexing, see here.
Use:
code :
df.loc[df.a==1,'c'] = df['b']
Comparing floats in a pandas column

Comparing floats in a pandas column


By : Rakan Elafrangi
Date : March 29 2020, 07:55 AM
it fixes the issue Due to imprecise float comparison you can or your comparison with np.isclose, isclose takes a relative and absolute tolerance param so the following should work:
code :
df['result'] = df['actual_credit'].ge(df['min_required_credit']) | np.isclose(df['actual_credit'], df['min_required_credit'])
Pandas: Find max value in column and print its row

Pandas: Find max value in column and print its row


By : Keith Conroy
Date : March 29 2020, 07:55 AM
it should still fix some issue You can try idxmax of inverted df by [::-1]:
code :
print df
                datetime           xra           xrb
1018 2016-02-21 17:00:00  1.000000e-09  2.380000e-07
1019 2016-02-21 17:00:01  1.000000e-09  2.380000e-07
1020 2016-02-21 17:00:02  1.000000e-09  2.380000e-07
1021 2016-02-21 17:01:00  1.080000e-09  2.370000e-07
1022 2016-02-21 17:02:00  1.000000e-09  2.350000e-07
1023 2016-02-21 17:03:00  1.000000e-09  2.340000e-07
1024 2016-02-21 17:04:00  1.000000e-09  2.330000e-07


print list(df.loc[df['xrb'][::-1].idxmax()])
[Timestamp('2016-02-21 17:00:02'), 1.0000000000000001e-09, 2.3800000000000001e-07]
print df
                datetime           xra           xrb
1020 2016-02-21 17:00:00  1.000000e-09  2.380000e-07
1020 2016-02-21 17:00:01  1.000000e-09  2.380000e-07
1020 2016-02-21 17:00:02  1.000000e-09  2.380000e-07
1021 2016-02-21 17:01:00  1.080000e-09  2.370000e-07
1022 2016-02-21 17:02:00  1.000000e-09  2.350000e-07
1023 2016-02-21 17:03:00  1.000000e-09  2.340000e-07
1024 2016-02-21 17:04:00  1.000000e-09  2.330000e-07

print df[df['xrb'] == df['xrb'].max()]
                datetime           xra           xrb
1020 2016-02-21 17:00:00  1.000000e-09  2.380000e-07
1020 2016-02-21 17:00:01  1.000000e-09  2.380000e-07
1020 2016-02-21 17:00:02  1.000000e-09  2.380000e-07
Any way to create a column of tuples from a column of floats in pandas?

Any way to create a column of tuples from a column of floats in pandas?


By : longnh
Date : March 29 2020, 07:55 AM
I wish did fix the issue. I'm given a list of tuples of the following form: , I would first create a Series from your list of tuples:
code :
LS = pd.Series(ls)
data['ls'] = data['ind'].map(LS)
ls = [(14, 6, 1.5), (14, 7, 1.5), (14, 8, 1.5), (14, 9, 1.5), (14, 10, 1.5), (14, 11, 1.5), (14, 12, 1.5)]
data = pd.DataFrame({'ind':[0,2,3]})
In [10]: LS = pd.Series(ls)

In [11]: LS
Out[11]: 
0     (14, 6, 1.5)
1     (14, 7, 1.5)
2     (14, 8, 1.5)
3     (14, 9, 1.5)
4    (14, 10, 1.5)
5    (14, 11, 1.5)
6    (14, 12, 1.5)
dtype: object

In [12]: data['ls'] = data['ind'].map(LS)

In [13]: data
Out[13]: 
   ind            ls
0    0  (14, 6, 1.5)
1    2  (14, 8, 1.5)
2    3  (14, 9, 1.5)
How to bin column of floats with pandas

How to bin column of floats with pandas


By : kadaj1st
Date : March 29 2020, 07:55 AM
it fixes the issue This is a good use case for pandas.cut:
code :
bins = [-np.inf, 1.2, 1.6, 2.0, 2.4, 2.8, 3.2, 3.6, np.inf]
labels = ['H', 'G', 'F', 'E', 'D', 'C', 'B', 'A']

df['ipk1'] = pd.cut(df['ipk1'], bins=bins, labels=labels)
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