How do I select and store columns greater than a number in pandas?

Question

I have a pandas DataFrame with a column of integers. I want the rows containing numbers greater than 10. I am able to evaluate True or False but not the actual value, by doing:

df['ints'] = df['ints'] > 10

I don’t use Python very often so I’m going round in circles with this.

I’ve spent 20 minutes Googling but haven’t been able to find what I need….

Edit:

    observationID   recordKey   gridReference   siteKey siteName    featureKey  startDate   endDate pTaxonVersionKey    taxonName   authority   commonName  ints
0   463166539   1767    SM90    NaN NaN 150161  12/02/2006  12/02/2006  NBNSYS0100004720    Pipistrellus pygmaeus   (Leach, 1825)   Soprano Pipistrelle 2006
1   463166623   4325    TL65    NaN NaN 168651  21/12/2008  21/12/2008  NHMSYS0020001355    Pipistrellus pipistrellus sensu stricto (Schreber, 1774)    Common Pipistrelle  2008
2   463166624   4326    TL65    NaN NaN 168651  18/01/2009  18/01/2009  NHMSYS0020001355    Pipistrellus pipistrellus sensu stricto (Schreber, 1774)    Common Pipistrelle  2009
3   463166625   4327    TL65    NaN NaN 168651  15/02/2009  15/02/2009  NHMSYS0020001355    Pipistrellus pipistrellus sensu stricto (Schreber, 1774)    Common Pipistrelle  2009
4   463166626   4328    TL65    NaN NaN 168651  19/12/2009  19/12/2009  NHMSYS0020001355    Pipistrellus pipistrellus sensu stricto (Schreber, 1774)    Common Pipistrelle  2009

Asked By: Vinyl Warmth

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Source

Answer 1

Sample DF:

In [79]: df = pd.DataFrame(np.random.randint(5, 15, (10, 3)), columns=list('abc'))

In [80]: df
Out[80]:
    a   b   c
0   6  11  11
1  14   7   8
2  13   5  11
3  13   7  11
4  13   5   9
5   5  11   9
6   9   8   6
7   5  11  10
8   8  10  14
9   7  14  13

present only those rows where b > 10

In [81]: df[df.b > 10]
Out[81]:
   a   b   c
0  6  11  11
5  5  11   9
7  5  11  10
9  7  14  13

Minimums (for all columns) for the rows satisfying b > 10 condition

In [82]: df[df.b > 10].min()
Out[82]:
a     5
b    11
c     9
dtype: int32

Minimum (for the b column) for the rows satisfying b > 10 condition

In [84]: df.loc[df.b > 10, 'b'].min()
Out[84]: 11

UPDATE: starting from Pandas 0.20.1 the .ix indexer is deprecated, in favor of the more strict .iloc and .loc indexers.

Answered By: MaxU – stand with Ukraine

Answer 2

You can also use query:

In [2]: df = pd.DataFrame({'ints': range(9, 14), 'alpha': list('ABCDE')})

In [3]: df
Out[3]: 
   ints alpha
0     9     A
1    10     B
2    11     C
3    12     D
4    13     E

In [4]: df.query('ints > 10')
Out[4]: 
   ints alpha
2    11     C
3    12     D
4    13     E

Answered By: rachwa

Answer 3

I am wondering why no one mentions the builtin functions pandas dataframe has is similar to isin method. Here is a quick example

 df = pd.DataFrame({'cost': [250, 150, 100], 'revenue': [100, 250, 300]},index=['A', 'B', 'C'])

   cost  revenue
A   250      100
B   150      250
C   100      300

Compare DataFrames for greater than inequality or equality elementwise.

df=df[df["cost"].ge(150)]

   cost  revenue
    A   250      100
    B   150      250

Answered By: Panagiotis Drakatos

How do I select and store columns greater than a number in pandas?

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