Pandas Tutorial- How to run analysis using numeric python (numpy) with example

Numpy and Pandas

Numpy is a python package that is used for scientific computing. It provides support for large multi-dimensional arrays and matrices. Pandas is a python library used for data manipulation and analysis. Having a solid knowledge of both libraries is extremely useful for feature engineering, data imputation, and model building

>>> import numpy as np

Some of the important attributes of a NumPy object are:

1. Ndim: displays the dimension of the array
2. Shape: returns a tuple of integers indicating the size of the array
3. Size: returns the total number of elements in the NumPy array
4. Dtype: returns the type of elements in the array, i.e., int64, character
5. Itemsize: returns the size in bytes of each item
6. Reshape: Reshapes the NumPy array

 # list 
bob =[1,2,3]
print(bob)
import numpy as np


# In[3]:


# 1-Dimensional array
tuna =[1,3,4,5,6,7,8]
np.array(tuna)

# In[4]:
# 2-Dimensional array
mat = ([1,2,3],[4,5,6],[5,6,7])
np.array(mat)


# In[5]:
#note that when dealing with 2-D  
# it always end with 2 ']]' of that 
# and if it is three of it ']]]' then it is 3-D


# In[6]:
np.arange(0,11)


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# when dealing with range in array we use arange
np.arange(0,11,2) # the last parameter is the step(even num)


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# means 9 dot(.)
np.zeros(9)+2


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np.zeros((2,4)) # 2 -> num of rows 
                # 4 -> num of columns


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np.ones((2,5))


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# In[12]:


# linspace will take the number of POINT(dot. ) that we want e.g below is 5
np.linspace(0,5,5)


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#creating identity matrix 
# it a 2-D -> number of rows = number of colums
np.eye(5, dtype=int)


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#the random library can also be used by 
from numpy.random import randint
randint(2,7) #it give a random num from btw 2->6


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# arrays of numbers = 1-D
np.random.rand(5)


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np.random.rand(5,5) # 2-D


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np.random.randn(4,2) #return num < 0


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# it give a random num btw the number but exclusive 100
np.random.randint(0,100)


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np.random.randint(56,100,10)
#10 means 10 random numbers will be displayed butexclusive 100


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#attribute of an array
arr = np.arange(25)
arr


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# reshape is used to return the data of an array
# e.g arr above is 25 element and can be RESHAPED as 5x5 = 25 
arr.reshape(5,5)


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rar = np.random.randint(0,51,10)
rar


# In[23]:


rar.reshape(2,5)


# In[93]:


#to return the maximum and minimum of an array  
rar


# In[94]:


rar.max()


# In[95]:


rar.min()


# In[96]:


#getting the index location of the max value 
rar.argmax()


# In[98]:


#getting the index location of the min value 
rar.argmin()


# In[99]:


#getting the shape of a vector = size of array (1-D)
rar.shape


# In[29]:


rar  = rar.reshape(2,5)
rar.shape


# In[106]:


#getting datatype
arr.dtype


# In[107]:


rar.dtype


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np.full((5,4),3.14)


# In[24]:


mat = np.arange(1,26).reshape(5,5)
mat


# In[25]:


# NB all counting start from 0 for both rows and cloumn
mat[2:] # count row 2 down 


# In[26]:

mat[2:,1:] # 1: reps column 

# In[28]:

mat[3:,3:]

>>> import pandas as pd

Some commonly used data structures in pandas are:

1. Series objects: 1D array, similar to a column in a spreadsheet
2. DataFrame objects: 2D table, similar to a spreadsheet
3. Panel objects: Dictionary of DataFrames, similar to sheet in MS Excel

# In[1]:
import pandas as pd
# In[2]:
import numpy as np 
# In[20]:
labs = ['a','b','c']
my_data = [11,30,40]
arr = np.array(my_data)
d = { 'a': 20, 'b':30,'c':40}
# In[21]:
pd.Series(data = my_data)
# In[22]:
pd.Series(data = my_data, index=labs)
# In[23]:
#OR 
pd.Series(my_data,labs)
# In[24]:
pd.Series(arr,d)
# In[25]:
pd.Series(d)
# In[14]:
# a pandas series can hold a varities of numbers or object types 
# In[21]:
ser1 = pd.Series([1,2,3,4],['USA','Germany','USSR','Japan'])
ser1
# In[22]:
ser2 = pd.Series([1,2,6,4],['USA','Germany','Italy','Japan'])
# In[24]:
#passing the index of 1
ser1['USA']
# In[27]:
ser3 = pd.Series(labs, my_data)
ser3
# In[26]:
ser3[0] #index labs
# In[28]:
ser1  
# In[29]:
ser2
# In[30]:
ser1 + ser2  #integer is converted to float 
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