41 Python Pandas Series part 01

Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index. https://www.tutorialspoint.com/python_pandas/python_pandas_series.htm

In [1]:

import numpy as np
import pandas as pd

In [2]:

ser1=pd.Series(data=["venkat","balaji","vijay"],index=["dm","ds","rpa"])

In [3]:

ser1

Out[3]:

dm     venkat
ds     balaji
rpa     vijay
dtype: object

In [4]:

ser2=pd.Series(["venkat","balaji","Praven"],["dm","ds","ajs"])

In [5]:

ser2

Out[5]:

dm     venkat
ds     balaji
ajs    Praven
dtype: object

In [6]:

ser1+ser2

Out[6]:

ajs             NaN
dm     venkatvenkat
ds     balajibalaji
rpa             NaN
dtype: object

In [7]:

ser3=pd.Series(["a","c","d"])

In [8]:

ser3

Out[8]:

0    a
1    c
2    d
dtype: object

---

In [9]:

ser1["dm"]

Out[9]:

'venkat'

In [28]:

la = ['k','M1','z']
li = [10,20,30]
a1 = np.array(li)
di = {'a':10,'b':20,'c':30}

In [12]:

la

Out[12]:

['a', 'b', 'c']

In [13]:

li

Out[13]:

[10, 20, 30]

In [14]:

a1

Out[14]:

array([10, 20, 30])

In [15]:

di

Out[15]:

{'a': 10, 'b': 20, 'c': 30}

In [16]:

la = ['a','b','c']
li = [10,20,30]
a1 = np.array(li)
di = {'a':10,'b':20,'c':30}

In [17]:

se1=pd.Series(di)

In [18]:

se1

Out[18]:

a    10
b    20
c    30
dtype: int64

In [19]:

se2=pd.Series(data=li,index=la)

In [20]:

se2

Out[20]:

a    10
b    20
c    30
dtype: int64

In [22]:

se2=pd.Series(li,la)

In [23]:

se2

Out[23]:

a    10
b    20
c    30
dtype: int64

In [25]:

se2["c"]

Out[25]:

30

In [31]:

se3=pd.Series(la,di)

In [32]:

se3

Out[32]:

a     k
b    M1
c     z
dtype: object

In [35]:

se4=pd.Series(a1,la)

In [36]:

se4

Out[36]:

k     10
M1    20
z     30
dtype: int32