Schemas Used In Pandas Dataframes


Python Pandas DataFrame Join, Merge, and Concatenate

Schemas Used In Pandas Dataframes

In this tutorial, we are going to learn about Schemas Used In Pandas Dataframes.In last tutorial, my friends ask me to give us a proper description on schemas used in pandas dataframes.

But first we have to learn about dataframes.So let’s start our tutorial without any delay :-

Introduction
 DataFrame — pandas 1.0.3 documentation
A Data frame is a two-dimensional data structure, where data is aligned in a tabular fashion in rows and columns.

Features of Pandas DataFrame

·         Columns are of different types in datframes
·         Sizes are Mutable by nature
·         Labeled axes (rows and columns) in tabular form
·         We could Perform the Arithmetic operations on rows and columns

Create Your Own DataFrame

 Pandas loc insights (pd.DataFrame.loc) - Engineering

A basic DataFrame, which can be created Your Own Dataframe.

import pandas as pd
df = pd.DataFrame()
print (df)


Create a DataFrame from Lists

The DataFrame can be created using a single list or a list of lists.some example is given :-

import pandas as pd
data = [33,22,45,6,7,8,9]
df = pd.DataFrame(data)
print df

-----------------OR---------------------

import pandas as pd
data = [['pawan',103],['rahul',17],['pal',1356]]
df = pd.DataFrame(data,columns=['Name','id'])
print df

Create a DataFrame from Dictionaries

List of Dictionaries can be passed as input data to create a DataFrame. The dictionary keys are by default taken as column names.

The following example shows how to create a DataFrame by passing a list of dictionaries.

import pandas as pd
data = [{'a': 1, 'b': 2},{'a': 3, 'b': 4, 'c': 5}]
df = pd.DataFrame(data)
print df

Column Selection

We will understand this by selecting a column from the DataFrame.
Example

import pandas as pd

d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}

df = pd.DataFrame(d)
print df ['one']


Multiple schemas Used In Pandas Dataframe

import pandas as pd
import numpy as np

df = pd.DataFrame({
'col_1': [0, 1, 2, 3],
'col_2': [4, 5, 6, 7]
})
Combine schemas In Pandas Dataframes

df = df.join(pd.DataFrame(
{
'column_new_1': np.nan,
'column_new_2': 'humans',
'column_new_3': 3
}, index=df.index
))
Column Addition

We will understand this by adding a new column to an existing data frame.
Example

import pandas as pd

data = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}

df = pd.DataFrame(data)

Addition of Rows

Add new rows to a DataFrame using the append function. This function will append the rows at the end.
Example

import pandas as pd

df = pd.DataFrame([[1, 2], [3, 4]], columns = ['a','b'])
df2 = pd.DataFrame([[5, 6], [7, 8]], columns = ['a','b'])

df = df.append(df2)
print df


Deletion of Rows

Use index label to delete or drop rows from a DataFrame. If label is duplicated, then multiple rows will be dropped.

If you observe, in the above example, the labels are duplicate. Let us drop a label and will see how many rows will get dropped.

Example

import pandas as pd

df = pd.DataFrame([[1, 2], [3, 4]], columns = ['a','b'])
df2 = pd.DataFrame([[5, 6], [7, 8]], columns = ['a','b'])

df = df.append(df2)

---------OR------------
df = df.drop(0)

print (df)

This is the the end of our tutorial guys.

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Thank you Guys and Good Bye...

Keep learning,

Tech Prog

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