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In [3]:

    
import numpy as np



In [4]:

    
import pandas as pd



In [5]:

    
labels  = ["A","B","C"]



In [6]:

    
my_data = [10,20,30]



In [7]:

    
arr = np.array(my_data)
d  = {"A":10,"B":20,"C":30}



In [8]:

    
d









    Out[8]:





{'A': 10, 'B': 20, 'C': 30}



In [9]:

    
pd.Series(my_data,labels)









    Out[9]:





A    10
B    20
C    30
dtype: int64



In [11]:

    
pd.Series(arr,labels)









    Out[11]:





A    10
B    20
C    30
dtype: int64



In [12]:

    
pd.Series(d)









    Out[12]:





A    10
B    20
C    30
dtype: int64



In [13]:

    
pd.Series(labels,labels)









    Out[13]:





A    A
B    B
C    C
dtype: object



In [16]:

    
pd.Series([1,2,4],["USA","INDIA","USSR"])









    Out[16]:





USA      1
INDIA    2
USSR     4
dtype: int64



In [18]:

    
x = pd.Series([6,7,"India Is ..."],["India","USA","USSR"])



In [19]:

    
x["India"]









    Out[19]:





6



In [20]:

    
x["USSR"] = "India is an ally"



In [21]:

    
x









    Out[21]:





India                   6
USA                     7
USSR     India is an ally
dtype: object



In [24]:

    
np.randn(5,5)









    



---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-24-9f8043bad1be> in <module>()
----> 1 np.randn(5,5)

AttributeError: module 'numpy' has no attribute 'randn'



In [25]:

    
from numpy.random import randn



In [27]:

    
np.random.seed(101)



In [38]:

    
df = pd.DataFrame(randn(5,4),index='A B C D E'.split(),columns='W X Y Z'.split())



In [44]:

    
randn(5,4)
df



In [40]:

    
df["new-co"] = df["X"]



In [43]:

    
df.drop("new-co",axis=1)



In [45]:

    
df.drop("new-co",axis=1,inplace=True)



In [46]:

    
df



In [55]:

    
df[["W","Y","Z"]]



In [58]:

    
df["NEW"] = df["W"]



In [66]:

    
df.drop("NEW",axis=1,inplace=True)
df



In [63]:

    
df



In [68]:

    
df.loc["A"]









    Out[68]:





W    0.975720
X   -0.388239
Y    0.783316
Z   -0.708954
Name: A, dtype: float64



In [70]:

    
df.loc["E"]









    Out[70]:





W    0.305632
X    0.243178
Y    0.864165
Z   -1.560931
Name: E, dtype: float64



In [71]:

    
df.loc["D","Y"]









    Out[71]:





0.72902373213544547



In [74]:

    
df.loc[["A","E"],["X","Y"]]



In [75]:

    
df



In [76]:

    
df["Y"]









    Out[76]:





A    0.783316
B    0.677535
C   -0.532471
D    0.729024
E    0.864165
Name: Y, dtype: float64



In [77]:

    
df[df>0]



In [78]:

    
df["W"]









    Out[78]:





A    0.975720
B    0.586847
C   -1.678284
D    0.197524
E    0.305632
Name: W, dtype: float64



In [79]:

    
df[["W","Y","Z"]]



In [80]:

    
my_list = ["X","Y","Z"]



In [81]:

    
df[my_list]



In [82]:

    
df["New-Col"] = df["X"] + df["Y"]



In [83]:

    
df



In [84]:

    
df.drop("New-Col",axis=1,inplace=True)



In [85]:

    
df



In [86]:

    
df.loc["A"]









    Out[86]:





W    0.975720
X   -0.388239
Y    0.783316
Z   -0.708954
Name: A, dtype: float64



In [87]:

    
my_idf = ["A","B","C"]



In [88]:

    
df.loc[my_idf]



In [91]:

    
df.loc[my_idf,my_list]



In [94]:

    
x = df.index



In [95]:

    
x









    Out[95]:





Index(['A', 'B', 'C', 'D', 'E'], dtype='object')



In [96]:

    
df.loc[x]



In [97]:

    
df.columns









    Out[97]:





Index(['W', 'X', 'Y', 'Z'], dtype='object')



In [98]:

    
cols = df.columns



In [99]:

    
df.loc[x,cols]



In [101]:

    
df["W"]>.5









    Out[101]:





A     True
B     True
C    False
D    False
E    False
Name: W, dtype: bool



In [106]:

    
df[["X","Y"]]



In [119]:

    
df[df["W"]>0][["W","X"]]



In [118]:

    
df[df['W']>0][['W','X']]



In [121]:

    
df[df["Y"]>0][["X","Y"]]



In [123]:

    
boolx = df["W"]>0



In [124]:

    
boolx









    Out[124]:





A     True
B     True
C    False
D     True
E     True
Name: W, dtype: bool

df[boolx]



In [126]:

    
my_cols = ["X","Y"]



In [127]:

    
my_cols









    Out[127]:





['X', 'Y']



In [129]:

    
res = df[boolx]



In [130]:

    
res[my_cols]



In [141]:

    
df[(df["W"]>0) & (df["X"]>1)][["W","X","Y"]]



In [138]:

    
df



In [142]:

    
time.clock()









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-142-d0ea78653c97> in <module>()
----> 1 time.clock()

NameError: name 'time' is not defined



In [143]:

    
import time



In [145]:

    
x = time.time()



In [146]:

    
type(x)









    Out[146]:





float



In [147]:

    
x









    Out[147]:





1480926894.865967



In [153]:

    
x = pd.date_range('07:00:00', periods=10, freq='30min')



In [168]:

    
x
type(x)









    Out[168]:





pandas.tseries.index.DatetimeIndex



In [162]:

    
pd.date_range('07:00:00', periods=10, freq='15min')









    Out[162]:





DatetimeIndex(['2016-12-05 07:00:00', '2016-12-05 07:15:00',
               '2016-12-05 07:30:00', '2016-12-05 07:45:00',
               '2016-12-05 08:00:00', '2016-12-05 08:15:00',
               '2016-12-05 08:30:00', '2016-12-05 08:45:00',
               '2016-12-05 09:00:00', '2016-12-05 09:15:00'],
              dtype='datetime64[ns]', freq='15T')



In [165]:

    
datetime  = 6



In [166]:

    
datetime









    Out[166]:





6



In [167]:

    
type(datetime)









    Out[167]:





int



In [171]:









    



---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-171-490ec51554aa> in <module>()
----> 1 datetime.time

AttributeError: 'int' object has no attribute 'time'



In [170]:

    
from datetime import timedelta



In [172]:

    
def add_nums(x,y):
    return x+y
add_nums(2,2)









    Out[172]:





4



In [174]:

    
def add_numbers(x,y,z):
    return x+y+z

add_numbers(1,2,3)









    Out[174]:





6



In [179]:

    
def do_math(a,b,kind)
  if (kind == 'add'):
    return a+b
  else:
    return a-b









    



  File "<ipython-input-179-a80a9d766e4f>", line 1
    def do_math(a,b,kind)
                         ^
SyntaxError: invalid syntax



In [180]:

    
datetime.datetime.strptime('03:55', '%H:%M').time()









    



---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-180-e3dc90cff41b> in <module>()
----> 1 datetime.datetime.strptime('03:55', '%H:%M').time()

AttributeError: 'int' object has no attribute 'datetime'



In [181]:

    
datetime.time(*map(int, '03:55'.split(':')))
datetime.time(3, 55)









    



---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-181-29f1184566d6> in <module>()
----> 1 datetime.time(*map(int, '03:55'.split(':')))
      2 datetime.time(3, 55)

AttributeError: 'int' object has no attribute 'time'



In [183]:

    
lst = [1,2]



In [182]:

    
lst2 = [3,4]



In [184]:

    
z = lst + lst2



In [185]:

    
z









    Out[185]:





[1, 2, 3, 4]



In [186]:

    
lst*3









    Out[186]:





[1, 2, 1, 2, 1, 2]



In [189]:

    
lst[0]*3









    Out[189]:





3



In [188]:

    
lst[1]*3









    Out[188]:





6



In [190]:

    
1 in lst









    Out[190]:





True



In [191]:

    
6 in lst









    Out[191]:





False



In [ ]:

	W	X	Y	Z
A	0.975720	NaN	0.783316	NaN
B	0.586847	NaN	0.677535	0.026105
C	NaN	0.333973	NaN	2.117727
D	0.197524	2.302987	0.729024	NaN
E	0.305632	0.243178	0.864165	NaN

	W	X	Y	Z	new-co
A	0.975720	-0.388239	0.783316	-0.708954	-0.388239
B	0.586847	-1.621348	0.677535	0.026105	-1.621348
C	-1.678284	0.333973	-0.532471	2.117727	0.333973
D	0.197524	2.302987	0.729024	-0.863091	2.302987
E	0.305632	0.243178	0.864165	-1.560931	0.243178