Pandas — Operations

Pandas Dataframe Examples: Column Operations — #PySeries#Episode 14

print(“Hello Pandas operations!”)

Preparing our Notebook:

import numpy as np
import pandas as pd

The DataFrame:

df = pd.DataFrame({'col1':[1,2,3,4],
'col2':[444,555,666,444],
'col3':[‘abc’,’def’,’ghi’,’xyz’]})

df.head()

Operations in a DataFrame

Finding Unique Values:

df['col2'].unique()

array([444, 555, 666])

Another form:

len(df['col2'].unique())

3

Yet other:

df['col2'].nunique()
3

Counting Values

df['col2'].value_counts()

Conditional Selections

df

Returning a DataFrame where ‘col1’ happen to be greater than two:

df[df['col1']>2]

Combining Conditional Selection

df[(df['col1']>2) & (df['col2']==444)]

Apply Method

One of the powerful method in our tool belt When using Pandas;

We can grab a column and call a built-in function of it:

df['col2].sum()

2109

But we can apply our custom function:

def times2(x):
    return x*2

We can broadcast our function for each element in that column:

df['col2'].apply(times2)

Let’s go ahead and apply it with lambda expression:

This is probably the most powerful feature in Pandas: The ability to apply our custom lambda expression!

df[‘col2’].apply(lambda x:x*2)

Removing Columns

df

If you want that too occurs in place, we going to have to specify ‘implace=True’

df.drop('col1', axis=1)

Returning The Columns Name & Index Attributes

Columns:

df.columns

Index(['col1', 'col2', 'col3'], dtype='object')

Index:

df.index

RangeIndex(start=0, stop=4, step=1)

Sorting & Ordering a DataFrame

Just pass in the column we want to sort by:

df

df.sort_values('col2')

df.sort_values(by='col2')

Booleans

df.isnull()

Pivot Tables

The pivot table takes simple column-wise data as input and groups the entries into a two-dimensional table that provides a **multidimensional summarization of the data**

As we build up the pivot table, I think it’s easiest to take it one step at a time. Add items and check each step to verify you are getting the results you expect. Don’t be afraid to play with the order and the variables to see what presentation makes the most sense for your needs.

data=pd.DataFrame({'A':['foo','foo','foo','bar','bar','bar'],
'B':['one','one','two','two','one', 'one'],
'C':['x','y','x','y','x','y'],
'D':[1,3,2,5,4,1]})

#data

df=pd.DataFrame(data)

df

Pivot method takes 3 values: values, index, and columns:

df.pivot_table(values='D', index=['A','B'], columns='C')

print(“Thanks everyone! See you in the Next Pandas Lecture o/”)

Colab File link:)

Credits & References:

Jose Portilla — Python for Data Science and Machine Learning Bootcamp — Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

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