Pandas

Pandas is a Python-based framework for working with data . Pandas uses NumPy.

The author of Pandas is Wes McKinney. The initial release was in 2008.

The name Panda is from "panel data", a term from economics that refers to time series data.

This page is just a small start to an extensive system for manipulating data.

In Pandas, a series as a collection of data, such as a column in a spreadsheet.

In Pandas, a data frame is a collection of data, such as a table of rows and columns of a spreadsheet.

Data frames are often used for machine learning and other data science applications.

The following program shows the version of NumPy and Pandas being used.

Here is the Python code [#1]

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
print("Versions:")
print("\tnumpy: {0:s}".format(np.__version__))
print("\tpandas: {0:s}".format(pd.__version__))

Here is the output of the Python code.

Versions:
	numpy: 1.17.4
	pandas: 0.25.3

The central concepts in Pandas include the following.

A "series" is like a 1D array of data.
A data "frame" is like a 2D+ array of data

Both have support for various operations.

Here is the Python code [#2]

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
csvFs1 = "./data1.csv"
print("Loading CSV in \"{0:s}\"".format(csvFs1))
data1 = pd.read_csv(csvFs1)
# get first few lines using head
head1 = data1.head()
print("{0:s}".format(str(head1)))
# column operation
cName1 = "r0c2"
print("")
print("Getting column {0:s}:".format(cName1))
# get frame
col1 = data1[[cName1]]
print("")
print("Frame for {0:s}:".format(cName1))
print("{0:s}".format(str(col1)))
# get series
col1 = data1[cName1]
print("")
print("Series for {0:s}:".format(cName1))
print("{0:s}".format(str(col1)))
# get by index
col1 = data1.iloc[:,1]
print("")
print("Index for {0:s}:".format(cName1))
print("{0:s}".format(str(col1)))
# get row
row1 = data1.iloc[2]
print("")
print("Row 2:")
print("{0:s}".format(str(row1)))

Here is the output of the Python code.

Loading CSV in "./data1.csv"
   r0c1  r0c2  r0c3
0  r1c1  r1c2  r1c3
1  r2c1  r2c2  r2c3
2  r3c1  r3c2  r3c3
Getting column r0c2:
Frame for r0c2:
   r0c2
0  r1c2
1  r2c2
2  r3c2
Series for r0c2:
0    r1c2
1    r2c2
2    r3c2
Name: r0c2, dtype: object
Index for r0c2:
0    r1c2
1    r2c2
2    r3c2
Name: r0c2, dtype: object
Row 2:
r0c1    r3c1
r0c2    r3c2
r0c3    r3c3
Name: 2, dtype: object

Note that in a csv file, the data is a table and the first row is assumed to be the header row of the table.