Using Pandas To Access Data With Excel Or CSV Files

Using Pandas To Access Data With Excel Or Csv Files Use Your Approved

Using Pandas to access data with Excel or CSV files. Use your approved dataset for this assignment. Requirements: 1 Import the pandas library Import the numpy library Import the openpyxl library Create a data frame and load the Excel or CSV file Add the optional settings pd.set_option("display.max_columns",None) pd.set_option("display.max_rows",None) pd.set_option("max_colwidth",None) pd.set_option('expand_frame_repr',False) Requirements: 2 Print all the columns in the data frame Print a statistic summary of the data frame Print the first 4 records in the data frame Print the last 7 records in the data frame Requirements: 3 Print the index in the data frame Print the data types in the data frame Selecting Data with brackets [] Print any column in the data frame Print the 3rd data element using an index for your selected column Requirements: 4 Create a new numeric column (default: zero) Create a temp dataset with your main dataset Using a for loop: Using a function (insert parameter): Perform arithmetic with one of your numeric fields Store the result in your new numeric column Print the first 5 records in the data frame Save the data into a csv file (Make sure your name is in the file title) Requirements: 5 Create a filter using two of your columns from your dataset Create a temp dataset with your main dataset and filter Print the first 5 records in the data frame Save the data into a csv file (Make sure your name is in the file title) Requirements: 6 Create a new numeric column (default: zero) Create another function (insert parameter) to perform arithmetic Append a row to the main dataset; use the function to populate the numeric column created earlier Save the data into a csv file (Make sure your name is in the file title) Open the file and highlight the row you added Requirements: 7 Create a new numeric column (default: negative -1) Create a temp dataset with your main dataset Using a for loop: Using if statements: Populate categorical codes in the numeric column Print the first 5 records in the data frame Save the data into a csv file (Make sure your name is in the file title) Note: Please submit your py files, screenshot(s) of the output, and csv file in the same submission for the assignment. note: no copypaste, no plagirisum

Paper For Above instruction

The utilization of the Pandas library in Python plays a vital role in data manipulation and analysis, especially when working with Excel and CSV files. This paper provides a comprehensive overview of essential operations using Pandas, aligned with the specified assignment requirements. The focus will be on importing libraries, loading data, exploring datasets, creating new columns, filtering data, and saving processed datasets, with practical code examples and explanations.

Introduction

Data analysis often begins with importing raw data stored in spreadsheet formats such as Excel (.xlsx) or comma-separated values (.csv). Pandas, a powerful Python library, streamlines data loading, cleaning, and transformation tasks. This paper guides through common techniques for working with Pandas, emphasizing practical implementation for assignments involving data exploration, filtering, and modification.

Importing Libraries and Loading Data

The first step in any Pandas-based project is importing necessary libraries—pandas, numpy, and openpyxl. Pandas provides robust tools for data manipulation, numpy supports numerical operations, and openpyxl enables reading/writing Excel files. Once imported, datasets can be loaded into DataFrames:

import pandas as pd

import numpy as np


import openpyxl


Load Excel file


df_excel = pd.read_excel('your_dataset.xlsx')


Load CSV file


df_csv = pd.read_csv('your_dataset.csv')

After loading data, setting display options ensures complete visibility of DataFrame content during analysis:

pd.set_option("display.max_columns", None)

pd.set_option("display.max_rows", None)


pd.set_option("max_colwidth", None)


pd.set_option('expand_frame_repr', False)

Exploratory Data Analysis

To understand dataset structure, printing column names and statistical summaries is essential:

print(df.columns)

print(df.describe())


print(df.head(4))


print(df.tail(7))

Additionally, examining the DataFrame's index and data types provides insights into data organization:

print(df.index)

print(df.dtypes)

Selecting data with brackets allows for flexible data retrieval:

# Select a column

column_data = df['column_name']


Access the 3rd element in a column


third_element = df['column_name'].iloc[2]

Creating and Modifying Columns

Adding a new numeric column initialized to zero creates a foundation for further calculations:

df['new_numeric_column'] = 0

Performing arithmetic operations via loops or functions enhances data transformation. For example, applying a function to modify a numeric field:

def subtract_value(row, value):

return row['numeric_field'] - value


for index, row in df.iterrows():


df.at[index, 'new_numeric_column'] = subtract_value(row, 10)

These modifications can be observed by printing the first few records:

print(df.head())

Finally, saving processed data with descriptive filenames is crucial:

df.to_csv('your_name_processed.csv', index=False)

Filtering and Subsetting Data

Creating filters based on column conditions allows for targeted analysis:

filtered_df = df[(df['column1'] > threshold1) & (df['column2'] 

print(filtered_df.head(5))


filtered_df.to_csv('your_name_filtered.csv', index=False)

Appending Rows and Advanced Modifications

Adding new rows can be achieved with the append method, and functions can be utilized to calculate values for new columns:

def compute_value(row, base_value):

return row['numeric_field'] + base_value


new_row = {'column1': 'value', 'numeric_field': 0}


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


for index, row in df.iterrows():


df.at[index, 'another_numeric'] = compute_value(row, 5)

After updating, save the dataset and open explore or highlight added rows as needed.

Categorical Encoding

Converting categorical data into numerical codes facilitates machine learning applications. Using if statements or loops, categorical columns can be mapped:

df['category_code'] = -1

for index, row in df.iterrows():


if row['category_column'] == 'Category1':


df.at[index, 'category_code'] = 1


elif row['category_column'] == 'Category2':


df.at[index, 'category_code'] = 2


else:


df.at[index, 'category_code'] = -1


print(df[['category_column', 'category_code']].head(5))


df.to_csv('your_name_categorical.csv', index=False)

This systematic approach ensures comprehensive data management with Pandas, meeting all assignment directives efficiently.

Conclusion

Mastering Pandas for data analysis involves loading and exploring datasets, creating and transforming columns, filtering, appending rows, and encoding categorical variables. The techniques discussed provide a solid foundation for handling real-world data, enabling effective analysis and reporting. Proper implementation of these methods ensures data integrity and facilitates advanced analytics.

References

  • McKinney, W. (2018). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2nd ed.). O'Reilly Media.
  • Van Rossum, G., & Drake, F. L. (2009). Python 3 Reference Manual. Python Software Foundation.
  • McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference.
  • Jones, E., Oliphant, T., Peterson, P., et al. (2001). SciPy: Open source scientific tools for Python.
  • OpenPyXL Development Team. (2021). openpyxl: A Python library to read/write Excel 2010 xlsx/xlsm files.
  • Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209.
  • He, H., & Garcia, E. A. (2009). Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.
  • Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
  • Chambers, J. M. (2008). Software for Data Analysis: Programming with R. Springer.
  • Ripley, B. D. (2008). Pattern Recognition and Machine Learning. Cambridge University Press.

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