Table of contents

  1. How to import excel file and find a specific column using Pandas?
  2. How to find median and quantiles using Spark
  3. How to import a csv file using python with headers intact, where first column is a non-numerical

How to import excel file and find a specific column using Pandas?

To import an Excel file and find a specific column using pandas, follow these steps:

1. Install Required Libraries:

If you haven't installed the required libraries, you need both pandas and openpyxl:

pip install pandas openpyxl

openpyxl is a necessary dependency for reading .xlsx files.

2. Import Excel File:

Using the read_excel() function, you can import an Excel file into a DataFrame.

import pandas as pd

# Read the Excel file
df = pd.read_excel('path_to_file.xlsx', engine='openpyxl')

Replace 'path_to_file.xlsx' with the path to your actual Excel file.

3. Find a Specific Column:

If you know the column name, you can access it directly:

column_data = df['ColumnName']

However, if you're unsure of the column names, you can list all of them and then select the desired column:

# List all column names

# Select the column after identifying its name
column_data = df['DesiredColumnName']

If you're trying to find a column based on some criteria (e.g., columns that contain a specific string), you can do the following:

# Find columns that contain the word 'specific'
matching_columns = [col for col in df.columns if 'specific' in col]

# If you found any matching columns, access the first one
if matching_columns:
    column_data = df[matching_columns[0]]

Replace 'specific' with whatever keyword you're looking for. This will give you the data of the first column that contains the keyword. Adjust the code as necessary if you have different requirements.

How to find median and quantiles using Spark

To find the median and quantiles of a dataset using Spark, you can use the approxQuantile function provided by the pyspark.sql.functions module. This function uses an approximate algorithm based on the Greenwald-Khanna algorithm to compute quantiles efficiently.

Here's how you can find the median and quantiles using Spark:

from pyspark.sql import SparkSession
from pyspark.sql.functions import approxQuantile

# Create a Spark session
spark = SparkSession.builder.appName("QuantilesExample").getOrCreate()

# Sample data
data = [(1,), (2,), (3,), (4,), (5,)]
columns = ["value"]

# Create a DataFrame from the sample data
df = spark.createDataFrame(data, columns)

# Compute the median (50th percentile)
median = approxQuantile("value", [0.5], relativeError=0.01)[0]
print("Median:", median)

# Compute other quantiles
quantiles = approxQuantile("value", [0.25, 0.5, 0.75], relativeError=0.01)
print("Quantiles:", quantiles)

# Stop the Spark session

In this example, the approxQuantile function takes three arguments:

  • The column name for which you want to compute the quantiles.
  • A list of quantile values (0.5 for median, 0.25, 0.75 for quartiles, etc.).
  • The relativeError parameter controls the trade-off between accuracy and performance. Smaller values result in more accurate results but may take longer to compute.

The result of the approxQuantile function is a list of quantile values, with each quantile value corresponding to the specified quantile values in the input list.

Keep in mind that the approxQuantile function provides approximate quantile values. If you need exact quantile values, you might need to use different approaches or libraries.

How to import a csv file using python with headers intact, where first column is a non-numerical

To import a CSV file in Python while keeping the headers intact, and when the first column is non-numerical, you can use the csv module or a third-party library like pandas. Here, I'll show you how to do it with both approaches.

Using the csv module:

Suppose you have a CSV file named "data.csv" with the following content:

Alice,25,New York
Bob,30,Los Angeles

You can use the csv module to read this file:

import csv

file_path = "data.csv"

# Initialize an empty list to store the data
data = []

# Open and read the CSV file
with open(file_path, newline="") as csvfile:
    csvreader = csv.reader(csvfile)
    # Read the header row
    headers = next(csvreader)
    # Read the remaining rows
    for row in csvreader:

# Print the header and data
print("Headers:", headers)
for row in data:

This code will read the CSV file while preserving the headers, and you can access the data as a list of lists in the data variable.

Using pandas (recommended for data manipulation):

If you plan to work with the data extensively, using the pandas library is recommended as it simplifies data handling:

import pandas as pd

file_path = "data.csv"

# Read the CSV file using pandas
df = pd.read_csv(file_path)

# Print the DataFrame

With pandas, you get a DataFrame object that provides various convenient methods for data manipulation and analysis. The header row is automatically recognized, and the data is stored in a tabular format.

Choose the method that best suits your needs, depending on the complexity of your data and the operations you want to perform on it.

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