Pandas — DATAFRAMES

When should I use pandas DataFrame?#PySeries#Episode 31

Let’s see Pandas' DATAFRAMES again! Google collab notebook link:)

Pandas DATAFRAMES: The Primary Pandas Data Structure!

Fig 0. Numpy & Pandas together!

When should I use pandas DataFrame?

The Pandas DataFrame is a structure that contains two-dimensional data and its corresponding labels.

DataFrames are widely used in data science, machine learning, scientific computing, and many other data-intensive fields.

What Follows Example of how to use it:

Please, open your collab notebook and follow me:

01# First thing first. Importing the libraries:

import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np

02# Let’s create a simple Graphic now:

Getting acquainted with PANDAS DATAFRAME:

x = np.linspace(0, 10, 30)
y = np.sin(x)
plt.plot(x, y, 'o', color='black');

3# Now here is a Real Problem:

Suppose a local ice cream shop keeps track of how many ice cream they sell versus the noon temperature on that day.
Here are registers for 12 days in a roll:

4# Let’s Plot the Graph & Make a Linear Regression:

Creating The Graph’s Axis From Numpy Arrays(x & y):

from scipy import stats
# Defining x & y Axis as Numpy Array
x=np.array([215,325,185,332,406,522,412,614,544,421,445,408])
y=np.array([14.2,16.4,11.9,15.2,18.5,22.1,19.4,25.1,23.4,18.1,22.6,17.2])
# Linear Regression
a,b,correlation,p,error=stats.linregress(x,y)
print('Regression line: y=%.2fx+%.2f'% (a,b))
print('Correlation Coefficient: r=%.2f'% correlation)
# Plotting the Graph
plt.plot(x,y,'o',label='Original data')
f=a*x+b
plt.plot(x,f,'r',label='Regression Line')
plt.ylim(10, 30)
plt.legend()
plt.title("Ice Cream Sales for The Last 12 Days")
plt.xlabel('Sales')
plt.ylabel('Temp °C')
plt.show()

Ice Cream Sales vs Temperature; Regression line: y=0.03x+6.41; Correlation Coefficient: r=0.96.

🍦As you can see, the temperature 🍧 boosts the sales for the ice screen 🍨

5# Now Pandas DATAFRAMES Operations:

DATAFRAMES Can be thought of as a dict-like container for Series objects.

Creating Pandas DATAFRAMES From Dictionary (X & Y):

# Creating Pandas DATAFRAME by passing Disctionary to Pandas Dataframe METHOD:
d = {'X': [215,325,185,332,406,522,412,614,544,421,445,408], 'Y': [14.2,16.4,11.9,15.2,18.5,22.1,19.4,25.1,23.4,18.1,22.6,17.2]}
df = pd.DataFrame(data=d)
df.index = ['1°_dia','2°_dia', '3°_dia','4°_dia','5°_dia','6°_dia','7°_dia','8°_dia','9°_dia','10°_dia','11°_dia','12°_dia']
df.columns = [ 'Ice_Cream_Sales', 'Temperature_°C' ]
df

DATAFRAME: Ice Cream Sales vs Temperature.

6# Pandas DATAFRAMES — Using Brackets Notation:

DATAFRAMES — The primary Pandas data dict-like container structure!

Getting specific Column LIKE THIS: df[‘Specific_column’]…

# DATAFRAMES Can be thought of as a dict-like container for Series objects
# Here I am passing the Column NAME as string:
df['Temperature_°C']
1°_dia     215 
2°_dia     325 
3°_dia     185 
4°_dia     332 
5°_dia     406 
6°_dia     522 
7°_dia     412 
8°_dia     614 
9°_dia     544 
10°_dia    421 
11°_dia    445 
12°_dia    408 Name: Temperature_°C, dtype: int64

What type of object is it?

type(df['Temperature_°C'])
pandas.core.series.Series

…or like this: df[[‘List_of_Columns’]]:

# Here I am passing the Columns' LIST:
df[['Temperature_°C','Ice_Cream_Sales'] ]

This is a DATAFRAME object!

# We’ve got a DATAFRAME object:
type(df)
pandas.core.frame.DataFrame

Returning a SERIES object:

df.Ice_Cream_Sales
# Returning a SERIES object
df.Ice_Cream_Sales
1°_dia     14.2
2°_dia     16.4 
3°_dia     11.9 
4°_dia     15.2 
5°_dia     18.5 
6°_dia     22.1 
7°_dia     19.4 
8°_dia     25.1 
9°_dia     23.4 
10°_dia    18.1 
11°_dia    22.6 
12°_dia    17.2 Name: Ice_Cream_Sales, dtype: float64

Returning a DATAFRAME object:

df[[‘Ice_Cream_Sales’]]

This is a DATAFRAME object!

7# Creating a New Column (X.Y):

Making a Multiple Operation with DATAFRAMES:

df['X.Y'] = df[ 'Temperature_°C'] * df['Ice_Cream_Sales']
df

8# Dropping Columns:

When inplace = True, the data is modified in place, which means it will return nothing, and the dataframe is now updated.

When inplace = False (DEFAULT), which is the default, then the operation is performed and it returns a copy of the object. You then need to save it to something.

df.drop('X.Y', axis=1, inplace=False)

df

df.drop('X.Y', axis=1, inplace=True)

Now:

df

9# Dropping Rows:

df.drop('12°_dia', axis=0)

df.shape
(12, 2)

Now with inplace attribute:

df.drop(‘12°_dia’, axis=0, inplace=True)
df

df.shape
(11, 2)

10# Selecting Rows — There are two methods:

LOC -> LABEL-BASE index

ILOC -> NUMERICAL-BASE index

# Calling the 11th day (loc): LABEL-BASE
df.loc['11°_dia']
Temperature_°C     445.0 
Ice_Cream_Sales     22.6 
Name: 11°_dia, dtype: float64

Now:

# Calling the 11th day (iloc):  NUMERICAL-BASE
df.iloc[10]
Temperature_°C     445.0 
Ice_Cream_Sales     22.6 
Name: 11°_dia, dtype: float64

11# Returning a Single Value:

# Calling the 9th day (loc): label-BASE
df.loc[['9°_dia'],['Ice_Cream_Sales']]

# Calling the Row 9th x Column 'Ice_Cream_Sales'  day (iloc):  Numerical-BASE
df.iloc[8,1]
23.4

12 # Returning a SUB-SET of the DataFrame:

df.loc[['7°_dia','8°_dia', '9°_dia'],['Ice_Cream_Sales']]

# Saving into a variable:
df2 = df.loc[['7°_dia', '9°_dia'],['Ice_Cream_Sales']]
df2

# Discovering the type's Variable:
type(df2)
pandas.core.frame.DataFrame

13 # J3 signing-off ;):

I WISH YOU ALL THE BEST!

print("That's it! This is another example for PySeries#Episode 08")

OK! That’s all!

I hope you enjoyed that lecture.

If you find this post helpful, please click the applause button and subscribe to the page for more articles like this one.

Until next time!

I wish you an excellent day!

31_pandas_dataframe_practice.ipynb

Credits & References

Based on: Support Vector Machines: A Visual Explanation with Sample Python Code by Alice Zhao

Related Post:

08 # PySeries#Episode 08 — Pandas — DataFrames : The Primary Pandas Data Structure! It Is a Dict-Like Container for Series Object