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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 
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Select Pandas Rows With Column Values Greater Than or Smaller Than Specific Value. To select Pandas rows with column values greater than or smaller than specific value, we use operators like > , <= , >= while creating masks or queries.
Pandas DataFrame: ge() functionThe ge() function returns greater than or equal to of dataframe and other, element-wise. Equivalent to ==, =!, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison. Any single or multiple element data structure, or list-like object.
Select Data Using Location Index (. This means that you can use dataframe. iloc[0:1, 0:1] to select the cell value at the intersection of the first row and first column of the dataframe. You can expand the range for either the row index or column index to select more data.
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.
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