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How to Check Column Nulls and Replace: Pandas

Here is a post that shows how to count Nulls and replace them with the value you want in the Pandas Dataframe. We have explained the process in two steps - Counting and Replacing the Null values. Count null values (column-wise) in Pandas ## count null values column-wise null_counts = df.isnull(). sum() print(null_counts) ``` Output: ``` Column1    1 Column2    1 Column3    5 dtype: int64 ``` In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then, we use the `isnull()` function to create a DataFrame of the same shape as `df`, where each element is a boolean value indicating whether that element is null or not. Finally, we use the `sum()` function to count the number of null values in each column of the resulting DataFrame. The output shows the count of null values column-wise. to count null values column-wise: ``` df.isnull().sum() ``` ##Code snippet to count null values row-wise: ``` df.isnull().sum(axis=1) ``` In the above code, `df` is the Panda

How to Effectively Parse and Read Different Files in Python

Here is Python logic that shows Parse and Read Different Files in Python. The formats are XML, JSON, CSV, Excel, Text, PDF, Zip files, Images, SQLlite, and Yaml.

Python Reading Files

Parse and Read Different Files in Python

import pandas as pd
import json
import xml.etree.ElementTree as ET
from PIL import Image
import pytesseract
import PyPDF2
from zipfile import ZipFile
import sqlite3
import yaml

Reading Text Files

# Read text file (.txt)
def read_text_file(file_path):
    with open(file_path, 'r') as file:
        text = file.read()
    return text

Reading CSV Files

# Read CSV file (.csv)
def read_csv_file(file_path):
    df = pd.read_csv(file_path)
    return df

Reading JSON Files

# Read JSON file (.json)
def read_json_file(file_path):
    with open(file_path, 'r') as file:
        json_data = json.load(file)
    return json_data

Reading Excel Files

# Read Excel file (.xlsx, .xls)
def read_excel_file(file_path):
    df = pd.read_excel(file_path)
    return df

Reading PDF files

# Read PDF file (.pdf)
def read_pdf_file(file_path):
    with open(file_path, 'rb') as file:
        pdf_reader = PyPDF2.PdfReader(file)
        text = ""
        for page in pdf_reader.pages:
            text += page.extract_text()
    return text

Reading XML Files

# Read XML file (.xml)
def read_xml_file(file_path):
    tree = ET.parse(file_path)
    root = tree.getroot()
    return root

Reading Image Files

# Read image file (.jpg, .png, etc.)
def read_image_file(file_path):
    image = Image.open(file_path)
    text = pytesseract.image_to_string(image)
    return text

Reading Zip Files

# Read compressed file (.zip, .tar.gz, etc.)
def read_compressed_file(file_path):
    with ZipFile(file_path, 'r') as zip_file:
        files = zip_file.namelist()
    return files

Reading SQLLite

# Read SQLite database file (.db)
def read_sqlite_file(file_path):
    conn = sqlite3.connect(file_path)
    cursor = conn.cursor()
    cursor.execute("SELECT * FROM table_name")
    data = cursor.fetchall()
    return data

Reading YAML Files

# Read YAML file (.yaml)
def read_yaml_file(file_path):
    with open(file_path, 'r') as file:
        yaml_data = yaml.load(file, Loader=yaml.SafeLoader)
    return yaml_data

# Usage examples
txt_file = "/path/to/text/file.txt"
txt_data = read_text_file(txt_file)

csv_file = "/path/to/csv/file.csv"
csv_dataframe = read_csv_file(csv_file)

json_file = "/path/to/json/file.json"
json_data = read_json_file(json_file)

excel_file = "/path/to/excel/file.xlsx"
excel_dataframe = read_excel_file(excel_file)

pdf_file = "/path/to/pdf/file.pdf"
pdf_text = read_pdf_file(pdf_file)

xml_file = "/path/to/xml/file.xml"
xml_data = read_xml_file(xml_file)

image_file = "/path/to/image/file.jpg"
image_text = read_image_file(image_file)

zip_file = "/path/to/compressed/file.zip"
compressed_files = read_compressed_file(zip_file)

sqlite_file = "/path/to/sqlite/file.db"
sqlite_data = read_sqlite_file(sqlite_file)

yaml_file = "/path/to/yaml/file.yaml"
yaml_data = read_yaml_file(yaml_file)

Note that some functionalities, like reading images or extracting data from an SQLite database, may require additional libraries to be installed, such as pytesseract for image processing and SQLite3 for database manipulation. Make sure you have those libraries installed before running the code.


In conclusion, the ability to read different file formats is a crucial skill in Python programming, enabling developers to handle a diverse range of data sources.


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