Basics in Pandas

Data cleaning: 

Data cleaning corrects issues in the structure and formatting of data, including missing values and unit conversions.

 

Exploratory data analysis (EDA):

EDA describe the process of transforming raw data to insightful observations. It is open-ended analysis of transforming, visualizaing, and summarizing patterns in data. 

# 'pd' is the conventional alias for Pandas, as 'np' is for NumPy
import pandas as pd

 

1. Series : 1-dimensional array

import pandas as pd

s = pd.Series([-1, 10, 2)
print(s)

'''
0	-1
1	10
2	2
dtype: int64
'''

s. array # Data contained within the Series
'''
<PandasArray>
[-1, 10, 2]
Length: 3, dtype: int64
'''

s.index # the Index of the Series
'''
RangeIndex(start=0, stop=3, step=1)
'''

 

Indices change

s = pd.Series([-1, 10, 2], index = ["a", "b", "c"])
print(s)
'''
a	-1
b	10
c	2
dtype: int64
'''

ser = pd.Series(4, -2, 0, 6], index = ["a", "b", "c", "d"])
print(ser)

'''
a	4
b	-2
c	0
d	6
dtype: int64
'''

print(ser["a"])
# 4

ser[["a", "c"]] # This return value is another Sereies
'''
a	4
c	0
dtype: int64
'''

 

A Filtering Condition

ser > 0 #Filter condition: select all elements greater than 0
'''
a	True
b	False
c	False
d	True
dtype: bool
'''

ser[ser > 0]
'''
a	4
d	6
dtype: int 64
'''

 

Data Frames

import pandas as pd

elections = pd.read_csv("data/elections.csv")
elections

 

Creating a DataFrame

1. Using a list and columns names

2. From a dictionary

3. From a Series

df_list = pd.DataFrame([1, 2, 3], columns=["Numbers"])
df_list

'''
	Numbers
0	1
1	2
2	3
'''

df_list = pd.DataFrame([[1, "one"], [2, "two"]], columns = ["Number", "Description"])
df_list

'''
	Number		Description
0	1		one
1	2		two
'''

 

From a Dictionary

df_dict = pd.DataFrame({"Fruit": ["Strawberry", "Orange"], "Price": [5.49, 3.99]})
df_dict

'''
	Fruit		Price
0	Strawberry	5.49
1	Orange		3.99
'''

 

From a Series

# Notice how our indices, or row labels, are the same

s_a = pd.Series(["a1", "a2", "a3"], index = ["r1", "r2", "r3"])
s_b = pd.Series(["b1", "b2", "b3"], index = ["r1", "r2", "r3"])

pd.DataFrame({"A-column": s_a, "B-column": s_b})

'''
	A-column	B-column
r1	a1		b1
r2	a2		b2
r3	a3		b3
'''