def _serialize(data, buf):
if isinstance(data, int):
data = cint(data)
elif isinstance(data, float):
data = cfloat(data)
if type(data) in [byte, short, cint, clong, cfloat, double]:
buf.write(STRUCTS[INVTYPES[type(data)]].pack(data))
elif isinstance(data, str):
write_string(data, buf)
elif isinstance(data, list):
buf.write(chr(data[0]))
buf.write(STRUCTS[3].pack(len(data) - 1))
for i in data[1:]:
_serialize(i, buf)
elif isinstance(data, dict):
for tag in data:
if isinstance(data[tag], ndarray):
buf.write(chr(ARRAYTYPES[data[tag].dtype.name]))
else:
buf.write(chr(INVTYPES[type(data[tag])]))
write_string(tag, buf)
_serialize(data[tag], buf)
buf.write(chr(0))
elif isinstance(data, ndarray):
value_str = data.tostring()
buf.write(
struct.pack(">I%ds" % (len(value_str), ), data.size, value_str))
def _serialize(data, buf):
if isinstance(data, int):
data = cint(data)
elif isinstance(data, float):
data = cfloat(data)
if type(data) in [byte, short, cint, clong, cfloat, double]:
buf.write(STRUCTS[INVTYPES[type(data)]].pack(data))
elif isinstance(data, str):
write_string(data, buf)
elif isinstance(data, list):
buf.write(chr(data[0]))
buf.write(STRUCTS[3].pack(len(data)-1))
for i in data[1:]:
_serialize(i, buf)
elif isinstance(data, dict):
for tag in data:
if isinstance(data[tag], ndarray):
buf.write(chr(ARRAYTYPES[data[tag].dtype.name]))
else:
buf.write(chr(INVTYPES[type(data[tag])]))
write_string(tag, buf)
_serialize(data[tag], buf)
buf.write(chr(0))
elif isinstance(data, ndarray):
value_str = data.tostring()
buf.write(struct.pack(">I%ds" % (len(value_str),), data.size, value_str))
reveal_type(np.short()) # E: {short}
reveal_type(np.intc()) # E: {intc}
reveal_type(np.intp()) # E: {intp}
reveal_type(np.int0()) # E: {intp}
reveal_type(np.int_()) # E: {int_}
reveal_type(np.longlong()) # E: {longlong}
reveal_type(np.ubyte()) # E: {ubyte}
reveal_type(np.ushort()) # E: {ushort}
reveal_type(np.uintc()) # E: {uintc}
reveal_type(np.uintp()) # E: {uintp}
reveal_type(np.uint0()) # E: {uintp}
reveal_type(np.uint()) # E: {uint}
reveal_type(np.ulonglong()) # E: {ulonglong}
reveal_type(np.half()) # E: {half}
reveal_type(np.single()) # E: {single}
reveal_type(np.double()) # E: {double}
reveal_type(np.float_()) # E: {double}
reveal_type(np.longdouble()) # E: {longdouble}
reveal_type(np.longfloat()) # E: {longdouble}
reveal_type(np.csingle()) # E: {csingle}
reveal_type(np.singlecomplex()) # E: {csingle}
reveal_type(np.cdouble()) # E: {cdouble}
reveal_type(np.complex_()) # E: {cdouble}
reveal_type(np.cfloat()) # E: {cdouble}
reveal_type(np.clongdouble()) # E: {clongdouble}
reveal_type(np.clongfloat()) # E: {clongdouble}
reveal_type(np.longcomplex()) # E: {clongdouble}
np.uint0() np.uint() np.ulonglong() np.half() np.single() np.double() np.float_() np.longdouble() np.longfloat() np.csingle() np.singlecomplex() np.cdouble() np.complex_() np.cfloat() np.clongdouble() np.clongfloat() np.longcomplex() np.bool_().item() np.int_().item() np.uint64().item() np.float32().item() np.complex128().item() np.str_().item() np.bytes_().item() np.bool_().tolist() np.int_().tolist() np.uint64().tolist()
print(
arraytobytes.tobytes("C")
) #C language print b'\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00'
print(
arraytobytes.tobytes("F")
) #Fortran language print b'\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00'
#39. Write a NumPy program to convert a given array into a list and then convert it into a list again.
givenarray = np.array([2, 3, 4, 5, 6])
print(givenarray) #print [2 3 4 5 6]
print(list(givenarray)) #print [2, 3, 4, 5, 6]
numpyfunctiontolist = givenarray.tolist()
print(numpyfunctiontolist) #print [2, 3, 4, 5, 6]
print(type(numpyfunctiontolist)) #print < class 'list' >
#40. Write a NumPy program to compute the x and y coordinates for points on a sine curve and plot the points using matplotlib.
#41. Write a NumPy program to convert numpy dtypes to native python types.
#Source: https://stackoverflow.com/questions/9452775/converting-numpy-dtypes-to-native-python-types
numpynumber = np.array([5, 7, 9])
print(numpynumber) #print [5 7 9]
numpyfloat = np.float32([5, 7, 9])
print(numpyfloat) #print [5. 7. 9.]
print(np.float64(numpyfloat)) #print [5. 7. 9.]
#print(type(np.float64(numpyfloat).item())) #print ValueError: can only convert an array of size 1 to a Python scalar
print(type(np.float64(0).item())) #print <class 'float'>
print(np.uint32(numpyfloat)) #print [5 7 9]
print(type(np.uint32(0).item())) #print <class 'int'>
print(np.cfloat(numpyfloat)) #print [5.+0.j 7.+0.j 9.+0.j] RM: complex
print(type(np.cfloat(0).item())) #print <class 'complex'>
reveal_type(np.short()) # E: numpy.signedinteger[numpy.typing._ reveal_type(np.intc()) # E: numpy.signedinteger[numpy.typing._ reveal_type(np.intp()) # E: numpy.signedinteger[numpy.typing._ reveal_type(np.int0()) # E: numpy.signedinteger[numpy.typing._ reveal_type(np.int_()) # E: numpy.signedinteger[numpy.typing._ reveal_type(np.longlong()) # E: numpy.signedinteger[numpy.typing._ reveal_type(np.ubyte()) # E: numpy.unsignedinteger[numpy.typing._ reveal_type(np.ushort()) # E: numpy.unsignedinteger[numpy.typing._ reveal_type(np.uintc()) # E: numpy.unsignedinteger[numpy.typing._ reveal_type(np.uintp()) # E: numpy.unsignedinteger[numpy.typing._ reveal_type(np.uint0()) # E: numpy.unsignedinteger[numpy.typing._ reveal_type(np.uint()) # E: numpy.unsignedinteger[numpy.typing._ reveal_type(np.ulonglong()) # E: numpy.unsignedinteger[numpy.typing._ reveal_type(np.half()) # E: numpy.floating[numpy.typing._ reveal_type(np.single()) # E: numpy.floating[numpy.typing._ reveal_type(np.double()) # E: numpy.floating[numpy.typing._ reveal_type(np.float_()) # E: numpy.floating[numpy.typing._ reveal_type(np.longdouble()) # E: numpy.floating[numpy.typing._ reveal_type(np.longfloat()) # E: numpy.floating[numpy.typing._ reveal_type(np.csingle()) # E: numpy.complexfloating[numpy.typing._ reveal_type(np.singlecomplex()) # E: numpy.complexfloating[numpy.typing._ reveal_type(np.cdouble()) # E: numpy.complexfloating[numpy.typing._ reveal_type(np.complex_()) # E: numpy.complexfloating[numpy.typing._ reveal_type(np.cfloat()) # E: numpy.complexfloating[numpy.typing._ reveal_type(np.clongdouble()) # E: numpy.complexfloating[numpy.typing._ reveal_type(np.clongfloat()) # E: numpy.complexfloating[numpy.typing._ reveal_type(np.longcomplex()) # E: numpy.complexfloating[numpy.typing._
#C4-FINBIM2
#filtramos aquellos con actividad posterior a inicio de 2o bim. (parti.)
len(d[(d['date'] > "2016-08-01") & (d['block'] == "C4")]['usrid'].value_counts())
####D4###
#cantidades iniciales de gente en el bloque
len(d[d['block'] == "D4"]['usrid'].value_counts())
#D4-FINBIM2
#filtramos aquellos con actividad posterior a inicio de 2o bim. (parti.)
len(d[(d['date'] > "2016-08-15") & (d['block'] == "D4")]['usrid'].value_counts())
if __name__ == '__main__':
main()
'''
import numpy as np
# examples using a.item()
type(np.float32(0).item()) # <type 'float'>
type(np.float64(0).item()) # <type 'float'>
type(np.uint32(0).item()) # <type 'long'>
# examples using np.asscalar(a)
type(np.asscalar(np.int16(0))) # <type 'int'>
type(np.asscalar(np.cfloat(0))) # <type 'complex'>
type(np.asscalar(np.datetime64(0))) # <type 'datetime.datetime'>
type(np.asscalar(np.timedelta64(0))) # <type 'datetime.timedelta'>
# In[87]:
import numpy as np
# for example, numpy.float32 -> python float
val = np.float32(0)
pyval = val.item()
print(type(pyval)) # <class 'float'>
# and similar...
type(np.float64(0).item()) # <class 'float'>
type(np.uint32(0).item()) # <class 'long'>
type(np.int16(0).item()) # <class 'int'>
type(np.cfloat(0).item()) # <class 'complex'>
type(np.datetime64(0, 'D').item()) # <class 'datetime.date'>
type(np.datetime64('2001-01-01 00:00:00').item()) # <class 'datetime.datetime'>
type(np.timedelta64(0, 'D').item()) # <class 'datetime.timedelta'>
# ### Better way to shuffle two numpy arrays in unison
# In[88]:
a = numpy.array([[[ 0., 1., 2.],
[ 3., 4., 5.]],
[[ 6., 7., 8.],
[ 9., 10., 11.]],