Python Pandas - DataFrame A Data frame is a two-dimensional ✓ Solved

A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns.

A pandas DataFrame can be created using the following constructor: pandas.DataFrame(data, index, columns, dtype, copy).

The parameters of the constructor are as follows:

  • data: data takes various forms like ndarray, series, map, lists, dict, constants and also another DataFrame.

  • index: For the row labels, the Index to be used for the resulting frame is optional. Default np.arange(n) if no index is passed.

  • columns: For column labels, the optional default syntax is np.arange(n). This is only true if no index is passed.

  • dtype: Data type of each column.

  • copy: This command is used for copying of data, if the default is False.

Create an Empty DataFrame. A basic DataFrame can be created as an Empty DataFrame. Example:

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. Example:

import pandas as pd


data = [1, 2, 3, 4, 5]


df = pd.DataFrame(data)


print(df)

Example 2:

import pandas as pd


data = [['Alex', 10], ['Bob', 12], ['Clarke', 13]]


df = pd.DataFrame(data, columns=['Name', 'Age'])


print(df)

Example 3:

import pandas as pd


data = [['Alex', 10], ['Bob', 12], ['Clarke', 13]]


df = pd.DataFrame(data, columns=['Name', 'Age'], dtype=float)


print(df)

Create a DataFrame from Dict of ndarrays/Lists. If index is passed, the length of the index should equal the length of the arrays. Example:

import pandas as pd


data = {'Name': ['Tom', 'Jack', 'Steve', 'Ricky'], 'Age': [28, 34, 29, 42]}


df = pd.DataFrame(data)


print(df)

Create a DataFrame from List of Dicts. The dictionary keys are taken as column names. Example:

import pandas as pd


data = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]


df = pd.DataFrame(data)


print(df)

Example 2:

import pandas as pd


data = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]


df = pd.DataFrame(data, index=['first', 'second'])


print(df)

Example 3:

import pandas as pd


data = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}] 


df = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b'])


print(df)

Column Selection:

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)


print(df['one'])

Column Addition:

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)


df['three'] = pd.Series([10, 20, 30], index=['a', 'b', 'c'])


print(df)

Column Deletion:

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']), 'three': pd.Series([10, 20, 30], index=['a', 'b', 'c'])}


df = pd.DataFrame(data)


del df['one']


print(df)

Row Selection, Addition, and Deletion:

Selection by Label:

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)


print(df.loc['b'])

Selection by Integer Location:

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)


print(df.iloc[2])

Add New Rows:

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:

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)


df = df.drop(0)


print(df)

Reporting and Factsheets with Pandas Overview:

Once you have the raw data in a DataFrame, it only requires a few lines of code to clean the data and slice & dice it into a digestible form for reporting.

Paper For Above Instructions

The Python pandas library is a powerful tool for data analysis and manipulation. A fundamental data structure in pandas is the DataFrame, which represents data in a tabular format, comprising rows and columns. This paper aims to provide a comprehensive overview of pandas DataFrames, their creation, manipulation techniques, and practical applications in data analysis.

Understanding DataFrame

A DataFrame is essentially a two-dimensional labeled data structure within the pandas library. It can be thought of as a table that holds data of different types: integers, floats, strings, etc. This uniqueness allows DataFrames to flexibly manage various data types simultaneously.

Creating DataFrames

There are several ways to create a DataFrame. One of the simplest methods is using a list of values. For example:

import pandas as pd


data = [1, 2, 3, 4, 5]


df = pd.DataFrame(data)


print(df)

This creates a DataFrame with a single column containing the numbers one to five. Additionally, DataFrames can be created from dictionaries, which allow more sophisticated structures with labeled indices:

data = {'Name': ['Tom', 'Jack', 'Steve'], 'Age': [28, 34, 29]}


df = pd.DataFrame(data)


print(df)

DataFrame from Lists

In scenarios where data is organized in lists, these lists can also be easily transformed into a DataFrame. A demonstration would be to create a DataFrame that represents various individuals and their ages:

data = [['Alex', 10], ['Bob', 12], ['Clarke', 13]]


df = pd.DataFrame(data, columns=['Name', 'Age'])


print(df)

This method provides a simple and effective way to organize multiple data points under appropriate headers.

DataFrame Indexing

Indexing in pandas is crucial for data manipulation. DataFrames allow the use of both labels and integer-based indexing. For example, users can select a specific row using the loc indexer for label-based selection:

print(df.loc[0])

This can be paired with the iloc indexer for integer-based selection. Such indexing capabilities enhance the data retrieval process significantly.

Manipulating DataFrames

DataFrames provide numerous methods for modifying and interacting with data. Users can add or delete columns with simplicity:

df['Height'] = [1.75, 1.80, 1.65]


print(df)


del df['Age']


print(df)

Moreover, appending new rows can be completed with the append() method that allows the user to concatenate additional data:

new_data = pd.DataFrame({'Name': ['Mike'], 'Height': [1.85]})


df = df.append(new_data, ignore_index=True)


print(df)

Applications of DataFrames

Pandas DataFrames have extensive applications in data analysis, ranging from data cleaning to data visualization. For instance, data scientists often utilize DataFrames to preprocess datasets by handling missing values, outliers, and performing transformations.

Furthermore, pandas integrates well with several plotting libraries such as Matplotlib, allowing direct visualization of DataFrame data:

import matplotlib.pyplot as plt


df['Age'] = [23, 25, 27]


df['Name'].value_counts().plot(kind='bar')


plt.show()

Conclusion

In summary, pandas DataFrames stand as a vital component for anyone engaged in data analysis. Their flexibility and user-friendly interface make it easier for users to work with data, allowing efficient manipulation and analysis. The comprehensive capabilities of DataFrames empower data scientists and analysts to derive insights and present findings effectively.

References