relatively new to pandas, I have a json and python files:
{"dataset":{
"id": 123,
"data": [["2015-10-16",1,2,3,4,5,6],
["2015-10-15",7,8,9,10,11,12],
["2015-10-14",13,14,15,16,17]]
}}
&
import pandas
x = pandas.read_json('sample.json')
y = x.dataset.data
print x.dataset
Printing x.dataset and y works fine, but when I go to access a sub-element y, it returns a 'buffer' type. What's going on? How can I access the data inside the array? Attempting y[0][1] it returns out of bounds error, and iterating through returns a strange series of 'nul' characters and yet, it appears to be able to return the first portion of the data after printing x.dataset...
The data attribute of a pandas Series points to the memory buffer of all the data contained in that series:
>>> df = pandas.read_json('sample.json')
>>> type(df.dataset)
pandas.core.series.Series
>>> type(df.dataset.data)
memoryview
If you have a column/row named "data", you have to access it by it's string name, e.g.:
>>> type(df.dataset['data'])
list
Because of surprises like this, it's usually considered best practice to access columns through indexing rather than through attribute access. If you do this, you will get your desired result:
>>> df['dataset']['data']
[['2015-10-16', 1, 2, 3, 4, 5, 6],
['2015-10-15', 7, 8, 9, 10, 11, 12],
['2015-10-14', 13, 14, 15, 16, 17]]
>>> arr = df['dataset']['data']
>>> arr[0][0]
'2015-10-16'
Related
I have a for loop but where i has changes by 2 and i want to save a value in a numpy array in each iteration that that changes by 1.
n = 8 #steps
# random sequence
rand_seq = np.zeros(n-1)
for i in range(0, (n-1)*2, 2):
curr_state= i+3
I want to get curr_state outside the loop in the rand_seq array (seven values).
can you help me with that?
thanks a lot
A much simpler version (if I understand the question correctly) would be:
np.arange(3, 15+1, 2)
where 3 = start, 15 = stop, 2 = step size.
In general, when using numpy try to avoid adding elements in a for loop as this is inefficient. I would suggest checking out the documentation of np.arange(), np.array() and np.zeros() as in my experience, these will solve 90% of array - creation issues.
A straight forward list iteration:
In [313]: alist = []
...: for i in range(0,(8-1)*2,2):
...: alist.append(i+3)
...:
In [314]: alist
Out[314]: [3, 5, 7, 9, 11, 13, 15]
or cast as a list comprehension:
In [315]: [i+3 for i in range(0,(8-1)*2,2)]
Out[315]: [3, 5, 7, 9, 11, 13, 15]
Or if you make an array with the same range parameters:
In [316]: arr = np.arange(0,(8-1)*2,2)
In [317]: arr
Out[317]: array([ 0, 2, 4, 6, 8, 10, 12])
you can add the 3 with one simple expression:
In [318]: arr + 3
Out[318]: array([ 3, 5, 7, 9, 11, 13, 15])
With lists, iteration and comprehensions are great. With numpy you should try to make an array, such as with arange, and modify that with whole-array methods (not with iterations).
cashflow = [0] + [10] * 7
# [0, 10, 10, 10, 10, 10, 10, 10]
for index in range(len(cashflow)):
growth_cashflow = 1.05**index * cashflow[index]
or
growth_cashflow = [1.05**index*pmt[index] for index in range(len(pmt))]
the result is:
[10.0, 10.5, 11.025, 11.576250000000002, 12.155062500000003, 12.762815625000004, 13.400956406250003]
But is it possible to get the same result with np.where?
cf = np.array(cashflow)
s = np.where(cf >= 0, 1.05**cf.index*cf, cf)
ERROR => AttributeError: 'numpy.ndarray' object has no attribute 'index'
Is it possible to get the index of each item and use it in the above multiplication?
If not, is there another way to do numpy without using for?
import numpy as np
cf=np.array([10, 10, 10, 10, 10, 10, 10])
s = cf*1.05**np.arange(len(cf))
print(s)
This should give you the output you are looking for. If you really want to get specific indices, you may want to use np.nonzero or np.argwhere.
I am (re)building up my knowledge of numpy, having used it a little while ago.
I have a question about fancy indexing with multidimenional (in this case 2D) arrays.
Given the following snippet:
>>> a = np.arange(12).reshape(3,4)
>>> a
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> i = np.array( [ [0,1], # indices for the first dim of a
... [1,2] ] )
>>> j = np.array( [ [2,1], # indices for the second dim
... [3,3] ] )
>>>
>>> a[i,j] # i and j must have equal shape
array([[ 2, 5],
[ 7, 11]])
Could someone explain in simple English, the logic being applied to give the results produced. Ideally, the explanation would be applicable for 3D and higher rank arrays being used to index an array.
Conceptually (in terms of restrictions placed on "rows" and "columns"), what does it mean to index using a 2D array?
Conceptually (in terms of restrictions placed on "rows" and "columns"), what does it mean to index using a 2D array?
It means you are constructing a 2d array R, such that R=A[B, C]. This means that the value for rij=abijcij.
So it means that the item located at R[0,0] is the item in A with as row index B[0,0] and as column index C[0,0]. The item R[0,1] is the item in A with row index B[0,1] and as column index C[0,1], etc.
So in this specific case:
>>> b = a[i,j]
>>> b
array([[ 2, 5],
[ 7, 11]])
b[0,0] = 2 since i[0,0] = 0, and j[0,0] = 2, and thus a[0,2] = 2. b[0,1] = 5 since i[0,0] = 1, and j[0,0] = 1, and thus a[1,1] = 5. b[1,0] = 7 since i[0,0] = 1, and j[0,0] = 3, and thus a[1,3] = 7. b[1,1] = 11 since i[0,0] = 2, and j[0,0] = 3, and thus a[2,3] = 11.
So you can say that i will determine the "row indices", and j will determine the "column indices". Of course this concept holds in more dimensions as well: the first "indexer" thus determines the indices in the first index, the second "indexer" the indices in the second index, and so on.
I want to use PandasUDFDType.SCALAR to operate the Row arrays like belows:
df = spark.createDataFrame([([1, 2, 3, 2],), ([4, 5, 5, 4],)], ['data'])
#pandas_udf(ArrayType(IntegerType()), PandasUDFType.SCALAR)
def s(x):
z = x.apply(lambda xx: xx*2)
return z
df.select(s(df.data)).show()
but it went wrong:
pyarrow.lib.ArrowInvalid: trying to convert NumPy type int32 but got int64```
I'm trying to slice into a multi-indexed data frame. I'm confused about conditions that generate IndexingError: Too many indexers. I'm also skeptical because I've found some bug reports about what may be this issue.
Specifically, this generates the error:
idx1 = [str(elem) for elem in [5, 6, 7, 8]]
idx2 = [str(elem) for elem in [10, 20, 30]]
index = pd.MultiIndex.from_product([idx1, idx2], names=('idx1', 'idx2'))
columns = ['m1', 'm2', 'm3']
df = pd.DataFrame(index=index, columns= columns)
df['m1'].loc[:,10]
That code above is trying to index into an index of dtypes of str, with an int, it seems to me. The error threw me off, as I don't understand why it says Too many indexers.
The below code works:
idx1 = [5, 6, 7, 8]
idx2 = [10, 20, 30]
index = pd.MultiIndex.from_product([idx1, idx2], names=('idx1', 'idx2'))
columns = ['m1', 'm2', 'm3']
df = pd.DataFrame(index=index, columns= columns)
df.loc[5,10] = [1,2,3]
df.loc[6,10] = [4,5,6]
df.loc[7,10] = [7,8,9]
type(df2['m1'])
df['m1'].loc[:,10]
There are some references to the same error: https://github.com/pandas-dev/pandas/issues/13597 which is marked closed and https://github.com/pandas-dev/pandas/issues/14885 which is open.
Is it ok to slice (a multi-indexed series) as in the lines above, assuming I get the dtype right? Also "Too many indexers" with DataFrame.loc
My pandas version is 20.3.