5 Pandas Basics Every Data Science Beginner Should Know!
Knowing these functions can kickstart your Data Analysis journey in Python
Why Pandas?
Pandas is very important library when it comes to Data Science in Python. Since it is built on top of the NumPy library, we can perform mathematical operations on arrays of single-type data(Series) and tables of data(DataFrame).
We can also quickly visualize the plots in a single line of code, without using Matplotlib library.
So, Pandas play a crucial role in Data related tasks in Python.
* Data Input and Output
Reading DataFrames from outside sources utilizing pd.read capacities.
CSV file
pd.read_csv()
pd.to_csv()
Excel file
pd.read_excel()
pd.to_excel()
* Shape of the Data
df.ndim
df.shape
* Display the Data
df.head()
df.tail()
* Selection and Indexing
df.iloc
df.loc
* Handling the Missing Values
df.dropna()
df.isnull().sum()
df.fillna()
These are some of the most functions, all data scientists use while working in Pandas. I’ll come up with another blog post to explain more about Pandas library. Thanks for reading!
