{"id":31651,"date":"2024-11-01T09:01:19","date_gmt":"2024-11-01T09:01:19","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=31651"},"modified":"2024-11-01T11:48:47","modified_gmt":"2024-11-01T11:48:47","slug":"python-data-types-pandas-dataframe","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/31651\/","title":{"rendered":"Python Data Types – Pandas DataFrame"},"content":{"rendered":"\n

Pandas DataFrame<\/h2>\n\n\n\n

Pandas<\/strong> is a library widely used for data analysis in Python, and among its features, DataFrame<\/strong> is a two-dimensional structure consisting of rows and columns. A DataFrame can store and manipulate data in a format similar to an Excel spreadsheet, making it very useful for data analysis tasks.<\/p>\n\n\n\n

import pandas as pd\n\n# Create a DataFrame\ndata = {\n    \"Name\": [\"Alice\", \"Bob\", \"Charlie\"],\n    \"Age\": [25, 30, 35],\n    \"City\": [\"New York\", \"Los Angeles\", \"Chicago\"]\n}\ndf = pd.DataFrame(data)\nprint(df)<\/code><\/pre>\n\n\n\n
Key Features of DataFrame<\/h3>\n\n\n\n
1. Creating a DataFrame<\/h3>\n\n\n\n
A DataFrame can be created from various data structures, such as dictionaries, lists, and Numpy arrays. For example, you can create a DataFrame using a dictionary.<\/p>\n\n\n\n
data = {\n    \"Product\": [\"Apple\", \"Banana\", \"Cherry\"],\n    \"Price\": [100, 200, 300]\n}\ndf = pd.DataFrame(data)\nprint(df)<\/code><\/pre>\n\n\n\n
2. Accessing Columns and Rows in a DataFrame<\/h3>\n\n\n\n
To access columns or rows in a DataFrame, you can use the loc<\/code> or iloc<\/code> methods. loc<\/code> accesses based on labels, while iloc<\/code> accesses based on integer indices.<\/p>\n\n\n\n
# Access a column\nprint(df[\"Product\"])\n\n# Access a row (using loc)\nprint(df.loc[0])\n\n# Access a row (using iloc)\nprint(df.iloc[1])<\/code><\/pre>\n\n\n\n
3. Adding and Removing Data<\/h3>\n\n\n\n
You can add new columns or rows to a DataFrame or delete existing data. To add a new column, you write as follows.<\/p>\n\n\n\n
# Adding a new column\ndf[\"Discounted Price\"] = df[\"Price\"] * 0.9\nprint(df)<\/code><\/pre>\n\n\n\n
To delete a row, use the drop()<\/code> method.<\/p>\n\n\n\n
# Deleting a row\ndf = df.drop(1)\nprint(df)<\/code><\/pre>\n\n\n\n
4. Data Analysis Functions<\/h3>\n\n\n\n
Pandas provides various functions useful for data analysis. For example, the describe()<\/code> function provides basic statistical information about the DataFrame.<\/p>\n\n\n\n
print(df.describe())<\/code><\/pre>\n\n\n\n
Additionally, you can use functions like mean()<\/code> and sum()<\/code> to calculate the average or sum of a specific column.<\/p>\n\n\n\n
average_price = df[\"Price\"].mean()\nprint(\"Average Price:\", average_price)<\/code><\/pre>\n\n\n\n
5. Filtering a DataFrame<\/h3>\n\n\n\n
You can filter data in a DataFrame based on specific conditions. For example, to select only products priced at 150 or more, write as follows.<\/p>\n\n\n\n
filtered_df = df[df[\"Price\"] >= 150]\nprint(filtered_df)<\/code><\/pre>\n\n\n\n
6. Sorting a DataFrame<\/h3>\n\n\n\n
To sort a DataFrame based on a specific column, use the sort_values()<\/code> method.<\/p>\n\n\n\n
# Sort in descending order by price\nsorted_df = df.sort_values(by=\"Price\", ascending=False)\nprint(sorted_df)<\/code><\/pre>\n\n\n\n
Summary<\/h2>\n\n\n\n