python: filter pandas data file based on several columns

Question

I am dealing with the analysis of dataset stored in pandas data file, which has been defined in my Python programm as

df = pd.DataFrame(columns=["System","LogP", "Hb_acc", "Hb_donnors", "Weight"])

According to the folat: the first two columns correspond to the number of the system and its name, and the columns 3-6 to the measurements performed for these systems:

        System    LogP Hb_acc Hb_donnors   Weight
0        cne_1  1.1732      3          1  263.405
1       cne_10  2.6639      2          0  197.237
2      cne_100 -0.2886      4          2  170.193
3     cne_1000  1.9644      5          1  304.709
4     cne_1001  1.4986      3          1  162.144
...        ...     ...    ...        ...      ...
1033   cne_995  3.0179      4          2  347.219
1034   cne_996  4.8419      6          2  407.495
1035   cne_997  3.3560      3          1  354.524
1036   cne_998  7.5465      4          2  635.316
1037   cne_999  3.3514      4          1  389.556

I need to filter these 1037 lines, taking only the lines matched the following demands for ALL of the columns :

the third_column (LogP), should be < 5
the fouth_column (Hb_acc), should be <10
the fifth_column (Hb_donor), should be <5
the six_column (Weight), should be < 500

ajgringo619 · Accepted Answer · 2020-12-01 22:52:01Z

0

You need to look at the pandas.DataFrame.loc (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html) method. As an example, your first request could be found like this:

new_df = df.loc[df['LogP'] < 5]

While I haven't had a need to (yet), I'd bet you can chain these together and get what you need in one shot.

answered Dec 1, 2020 at 22:52

ajgringo619

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1 Comment

user14418738 Over a year ago

Thank you! Actually I've made such filtering of DF considering all columns, using df_filter = df[(df['LogP'] < 5) & (df['Hb_acc'] < 10) & (df['Hb_donnors'] < 5) & (df['Weight'] < 500)] which is quite simular to your example. What is the .loc in you case ??

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