Python is one of the most useful programming languages for data analysis. Among its many libraries, Pandas makes it easier to explore, clean, and transform data. If you're starting your journey in Data Analytics or Data Science, these five ...
Python is one of the most useful programming languages for data analysis. Among its many libraries, Pandas makes it easier to explore, clean, and transform data.
If you're starting your journey in Data Analytics or Data Science, these five Pandas functions are a great place to begin.
1. head() — Preview Your Data
Use head() to view the first five rows of a dataset.
import pandas as pd
df = pd.read_csv("sales.csv")
print(df.head())
You can also use df.head(10) to display the first ten rows.
2. info() — Understand Your Dataset
This function provides information about columns, data types, and missing values.
df.info()
It helps you identify data types and columns that may need cleaning.
3. describe() — Get Statistical Insights
Use describe() to summarize numerical columns.
print(df.describe())
It displays statistics such as mean, standard deviation, minimum, and maximum.
4. isnull() — Find Missing Values
Missing data can affect your analysis. Use this function to identify it.
print(df.isnull().sum())
This shows the number of missing values in each column.
5. drop_duplicates() — Remove Duplicate Rows
Duplicate records can lead to inaccurate analysis.
df = df.drop_duplicates()
This removes duplicate rows from the DataFrame.
Practice Makes Progress
Try these functions on a sales, customer, or employee dataset. Once you understand them, explore Pandas functions such as groupby(), merge(), and fillna().
Which Pandas function do you use most often? Share your answer in the comments!
At Emulation Software Technologies, Sri Ganganagar, Rajasthan, we believe practical coding and continuous learning help build strong foundations in Data Analytics and Data Science.
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