Goodbye! Pandas

# Goodbye! Pandas

Hello everyone, I am Xiaohan.

In the field of Data Analysis, pandas is the cornerstone library for data manipulation and analysis in Python. Its powerful and flexible data structures enable us to perform complex operations with concise code.

Today, we will share 30 skills to improve your use of pandas.

#### 1. Embrace the power of read_csv parameters

Reading CSV files is a common task, but did you know read_csv more versatile than you think?

```Plaintext

import pandas as pd# Basic CSV read

df = pd.read_csv(‘data.csv’)# Advanced CSV read

df = pd.read_csv(‘data.csv’,

usecols=[‘Name’, ‘Age’, ‘Salary’],

dtype={‘Age’: ‘int8’, ‘Salary’: ‘float32’},

parse_dates=[‘Hire Date’],

skiprows=10)

```

By specifying the columns we need with the usecols parameter, we can reduce memory usage. Defining column types with dtype can further optimize performance. parse_dates ensure the date-time column is immediately available, skiprows allows us to skip header information or malformed rows.

#### 2. Method linking makes the code more concise

Method linking allows us to combine multiple operations into a single readable line.