The pandas library is designed to work with a panel or tabular data. pandas is a fast, highly efficient, and productive tool for manipulating and analyzing string, numeric, datetime, and time-series data. pandas provides data structures such as DataFrames and Series. A pandas DataFrame is a tabular, two-dimensional labeled and indexed data structure with a grid of rows and columns. Its columns are heterogeneous types. It has the capability to work with different types of objects, carry out grouping and joining operations, handle missing values, create pivot tables, and deal with dates. A pandas DataFrame can be created in multiple ways. Let's create an empty DataFrame:
# Import pandas library
import pandas as pd
# Create empty DataFrame
df = pd.DataFrame()
# Header of dataframe.
df.head()
Output:
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In the preceding example, we have created an empty DataFrame. Let's create a DataFrame using a dictionary of the list:
# Create dictionary of list
data = {&apos...
