Slice Pandas DataFrame by Row

Question

I am working with survey data loaded from an h5-file as hdf = pandas.HDFStore('Survey.h5') through the pandas package. Within this DataFrame, all rows are the results of a single survey, whereas the columns are the answers for all questions within a single survey.

I am aiming to reduce this dataset to a smaller DataFrame including only the rows with a certain depicted answer on a certain question, i.e. with all the same value in this column. I am able to determine the index values of all rows with this condition, but I can't find how to delete this rows or make a new df with these rows only.

Swede

Answer 1

In [36]: df
Out[36]:
   A  B  C  D
a  0  2  6  0
b  6  1  5  2
c  0  2  6  0
d  9  3  2  2

In [37]: rows
Out[37]: ['a', 'c']

In [38]: df.drop(rows)
Out[38]:
   A  B  C  D
b  6  1  5  2
d  9  3  2  2

In [39]: df[~((df.A == 0) & (df.B == 2) & (df.C == 6) & (df.D == 0))]
Out[39]:
   A  B  C  D
b  6  1  5  2
d  9  3  2  2

In [40]: df.ix[rows]
Out[40]:
   A  B  C  D
a  0  2  6  0
c  0  2  6  0

In [41]: df[((df.A == 0) & (df.B == 2) & (df.C == 6) & (df.D == 0))]
Out[41]:
   A  B  C  D
a  0  2  6  0
c  0  2  6  0

Asphyxia

is it possible to slice the dataframe and say (c = 5 or c =6) like THIS: ---> df[((df.A == 0) & (df.B == 2) & (df.C == 5 or 6) & (df.D == 0))] – Solberg

df[((df.A == 0) & (df.B == 2) & df.C.isin([5, 6]) & (df.D == 0))] or df[((df.A == 0) & (df.B == 2) & ((df.C == 5) | (df.C == 6)) & (df.D == 0))] – Asphyxia

Answer 2

If you already know the index you can use .loc:

In [12]: df = pd.DataFrame({"a": [1,2,3,4,5], "b": [4,5,6,7,8]})

In [13]: df
Out[13]:
   a  b
0  1  4
1  2  5
2  3  6
3  4  7
4  5  8

In [14]: df.loc[[0,2,4]]
Out[14]:
   a  b
0  1  4
2  3  6
4  5  8

In [15]: df.loc[1:3]
Out[15]:
   a  b
1  2  5
2  3  6
3  4  7

Edgington

It's worth a quick note that despite the notational similarity between df.loc[1:3] and some_list[1:3], the first uses an inclusive upper index while the second (and most of python) uses an exclusive upper index. – Sphalerite

Answer 3

If you just need to get the top rows; you can use df.head(10)

Bitternut

Answer 4

Use query to search for specific conditions:

In [3]: df
Out[3]: 
   age family   name
0    1      A   john 
1   36      A  jason 
2   32      A   jane 
3   26      B   jack 
4   30      B  james 

In [4]: df.query('age > 30 & family == "A"')
Out[4]: 
   age family   name
1   36      A  jason 
2   32      A   jane

Raster

After the query, how would I slice columns? For example, only display age and name. – Kakemono

Hi, if you want to use query, try df.query('age > 30 & family == "A"')[['age', 'name']]. Alternatively, you can use loc: df.loc[(df['age'] > 30) & (df['family'] == 'A'), ['age', 'name']]. – Raster