Link to the previous post : https://statinfer.com/104-3-5-box-plots-and-outlier-dectection-using-python/

In the last post we made box plot and understood how it is useful in detecting outliers.

In this post we will cover 3 most common plots for preliminary analysis.

Scatter Plot
Bar Chart
Trend Chart

Scatter Plot:

Scatter plots give us an indication on the relation between the two chosen variables.

Example:

cars=pd.read_csv("datasets\\Cars Data\\Cars.csv",encoding = "ISO-8859-1")
cars.shape

(428, 15)

cars.columns.values

array(['Make', 'Model', 'Type', 'Origin', 'DriveTrain', 'MSRP', 'Invoice',
       'EngineSize', 'Cylinders', 'Horsepower', 'MPG_City', 'MPG_Highway',
       'Weight', 'Wheelbase', 'Length'], dtype=object)

cars['Horsepower'].describe()

count    428.000000
mean     215.885514
std       71.836032
min       73.000000
25%      165.000000
50%      210.000000
75%      255.000000
max      500.000000
Name: Horsepower, dtype: float64

cars['MPG_City'].describe()

count    428.000000
mean      20.060748
std        5.238218
min       10.000000
25%       17.000000
50%       19.000000
75%       21.250000
max       60.000000
Name: MPG_City, dtype: float64

import matplotlib.pyplot as plt
plt.scatter(cars.Horsepower,cars.MPG_City)

<matplotlib.collections.PathCollection at 0xd272e10>

Practice : Creating Scatter Plots

Dataset: “./Sporting_goods_sales/Sporting_goods_sales.csv”
Draw a scatter plot between Average_Income and Sales. Is there any relation between two variables?
Draw a scatter plot between Under35_Population_pect and Sales. Is there any relation between two?

sports_data=pd.read_csv("datasets\\Sporting_goods_sales\\Sporting_goods_sales.csv",encoding = "ISO-8859-1")
sports_data.head(5)

	Sr_no	Avg_family_size	Average_Income	M_F_Gender_Ratio	Un_emp_rate	Under35_Population_pect	Number_schools	Sales
0	1	3	9305.306044	46.654268	2.587691	51.426218	395.379432	140870.7288
1	2	2	8907.622334	64.505029	2.731910	28.485052	316.503520	100305.7146
2	3	2	9846.602630	63.595331	4.269577	49.452727	359.077144	135474.6688
3	4	2	8871.731173	50.451251	3.124004	44.678507	346.833014	126349.5082
4	5	4	9891.047985	51.353801	2.004201	37.664024	329.034161	117434.7267

# Draw a scatter plot between Average_Income and Sales. Is there any relation between two variables?

import matplotlib.pyplot as plt
plt.scatter(sports_data.Average_Income ,sports_data.Sales)

<matplotlib.collections.PathCollection at 0xd2dbba8>

import matplotlib.pyplot as plt

plt.scatter(sports_data.Under35_Population_pect  ,sports_data.Sales)

<matplotlib.collections.PathCollection at 0xd4f9eb8>

Bar Chart

Bar charts used to summarize the categorical variables

freq=cars.Cylinders.value_counts()
freq.values

array([190, 136,  87,   7,   3,   2,   1], dtype=int64)

freq.index

Float64Index([6.0, 4.0, 8.0, 5.0, 12.0, 10.0, 3.0], dtype='float64')

import matplotlib.pyplot as plt

plt.bar(freq.index,freq.values)

<Container object of 4 artists>

Practice : Bar Chart

Dataset: “./Sporting_goods_sales/Sporting_goods_sales.csv”
Create a bar chart summarizing the information on family size.

freq=sports_data.Avg_family_size.value_counts()
freq.values

array([61, 57, 18, 14], dtype=int64)

freq.index

Int64Index([3, 2, 4, 1], dtype='int64')

import matplotlib.pyplot as plt
plt.bar(freq.index,freq.values)

<Container object of 4 artists>

Trend chart

Trend chart is used for time series datasets

AirPassengers=pd.read_csv("datasets\\Air Travel Data\\Air_travel.csv", encoding = "ISO-8859-1")
AirPassengers.head()

	DATE	AIR
0	JAN49	112
1	FEB49	118
2	MAR49	132
3	APR49	129
4	MAY49	121

AirPassengers.columns.values

array(['DATE', 'AIR'], dtype=object)

import matplotlib.pyplot as plt
plt.plot(AirPassengers.AIR)

[<matplotlib.lines.Line2D at 0xd55ff98>]

20th June 2017

104.3.6 Creating Graphs in Python

Visualizing data in Python.

Scatter Plot:

Practice : Creating Scatter Plots

Bar Chart

Practice : Bar Chart

Trend chart

Statinfer

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Statinfer

104.3.6 Creating Graphs in Python

Visualizing data in Python.

Scatter Plot:

Practice : Creating Scatter Plots

Bar Chart

Practice : Bar Chart

Trend chart

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