Panda's

Question 1

Panda data structure Series. what is it?

Answer 1

A Series is a one-dimentional array-like object, including a sequence of value (similar to NumPy array) and an associated array of index.
obj=pd.Series([4,5,-3,2])
obj
0    4
1    5
2   -3
3    2
dtype: int64

Question 2

output the array values

Answer 2

obj.values

array([ 4, 5, -3, 2], dtype=int64)

Question 3

output the list of index values in panda series

Answer 3

obj.index

RangeIndex(start=0, stop=4, step=1)

Question 4

how to assign a different index in panda series

Answer 4

#Specify a different index
obj2=pd.Series([4,5,-3,2],index=['d','c','a','b'])
obj2

Question 5

get value from index in panda series

Answer 5

pandas has more fexibility to use index than NumPy.

obj2[‘c’]
5

Question 6

show same index but use normal index position

Answer 6

still works.
#pandas has more fexibility to use index than NumPy.
obj2[1]
5

Question 7

get 2 values using the assigned letters in panda series.

Answer 7

obj2[[‘a’,’d’]]
#[‘a’,’d’] can be seen as a list of indices. It returns to a subset of the original Seires, which is also a Seiries.
a -3
d 4

Question 8

you can do numpy like operations on the series array.

Answer 8

obj2[obj2>0]
d    4
c    5
b    2
dtype: int64

Question 9

find data type

Answer 9

type(new)

Question 10

find missing data in pandas

Answer 10

pd. isnull(obj4)

obj3. isnull()

Question 11

bool to find missing data

Answer 11

pd.notnull(obj4)

Question 12

assign value 300 to bread

Answer 12

obj4[‘bread’]=300

Question 13

DataFrame

Answer 13

DataFrame¶
There are many possible data inputs to DataFrame. Such as, np array, dict of lists ot tuples, dict of Series, dict of dicts and so on…

We only intorudce how to contruct DataFrame through dict of lists

Question 14

create a dataframe

Answer 14

create a DataFrame through a dict of equal length lists or NumPy arrays:

data={‘state’:[‘Ohio’,’Ohio’,’Ohio’,’Nevada’,’Nevada’,’Nevada’],
‘year’:[2000,2001,2002,2000,2001,2002],
‘pop’:[1.5,1.7,3.6,2.4,2.9,3.2]}
frame=pd.DataFrame(data)
frame

	state	year	       pop
0	Ohio	2000	1.5
1	Ohio	2001	1.7
2	Ohio	2002	3.6
3	Nevada	2000	2.4
4	Nevada	2001	2.9
5	Nevada	2002	3.2

Question 15

create another dataframe from dictionary

Answer 15

election = {'state':['New Jersey','Ohio','West Virginia'],
           'Winner':['Hillary','Trump','Trump'],
                     'Margin':[5,7,15]}
election
type(election)
electionresult = pd.DataFrame(election)
#electionresult
electionresult.head()

Question 16

show first 2 indexes of dataframe

Answer 16

electionresult2=pd.DataFrame(electionresult,index=[0,1])

electionresult2

Question 17

are lists mutable?

Answer 17

You have to understand that Python represents all its data as objects. … Some of these objects like lists and dictionaries are mutable , meaning you can change their content without changing their identity. Other objects like integers, floats, strings and tuples are objects that can not be changed.

Question 18

create a Series

Answer 18

a=pd.Series([1,2,3,4],[‘a’,’b’,’c’,’d’])

Question 19

show the data in a series

Answer 19

a.values

array([ 4, 5, -3, 2])

Question 20

show the index with pandas

Answer 20

a.index

Question 21

by using labels. #pandas has more fexibility to use index than NumPy.

Answer 21

a[‘c’]=5
a[‘c’]
5

Question 22

numpy like operations

Answer 22

obj2[obj2>0]

np.exp(obj2)

Question 23

create a series from a dictionary

Answer 23

dict1={'eggs':10,'ham':20}
series1=pd.Series(dict1)
series1
eggs    10
ham     20
dtype: int64

Question 24

how to find if something exists in series

Answer 24

pd.isnull(obj4)
bread False
ham False
dtype: bool

Question 25

give value to a key in Series

Answer 25

obj4[‘bread’]=300
obj4
300

Question 26

add the values of 2 series

Answer 26

print(obj3)
print(obj4)
obj4+obj3

Question 27

select first 5 rows of a dataframe

Answer 27

frame.head()#this method selects only the first 5 rows.

Question 28

first 10 rows of a dataframe

Answer 28

frame[:10]

Question 29

change the index on a dataframe

Answer 29

frame=pd.DataFrame(data,index=[1,2,3,4,5,6])
frame
you can also do
frame=pd.DataFrame(data,index=['a','b',3,4,5,6])
      state	        year	pop
1	Ohio	2000	1.5
2	Ohio	2001	1.7
3	Ohio	2002	3.6
4	Nevada	2000	2.4
5	Nevada	2001	2.9
6	Nevada	2002	3.2

Question 30

create dataframe with some index numbers of your choosing and column headers

Answer 30

frame2=pd.DataFrame(data,index=[0,2,3,4,6,4],columns=[‘year’,’state’,’pop’,’debt’])#columns are arranged in order
frame2

Question 31

show a specific column of a dataframe

Answer 31

either works:
#frame2.year #notice the index has been overidden.
frame2[‘year’]

Question 32

replace value on every row of a specific column

Answer 32

frame2['debt']=16.5
frame2
	year	state	pop	      debt
1	2000	Ohio	1.5	16.5
2	2001	Ohio	1.7	16.5
3	2002	Ohio	3.6	16.5
4	2000	Nevada	2.4	16.5
5	2001	Nevada	2.9	16.5
6	2002	Nevada	3.2	16.5

Question 33

add a bunch of values in series on dataframe for specific column

Answer 33

#how did we asign float number 1.0-6.0 to the debt. 
frame2['debt']=np.arange(1.0,7.0,1.0)
frame2
	year	state	pop      debt
1	2000	Ohio	1.5	1.0
2	2001	Ohio	1.7	2.0
3	2002	Ohio	3.6	3.0
4	2000	Nevada	2.4	4.0
5	2001	Nevada	2.9	5.0
6	2002	Nevada	3.2	6.0