pandas.DataFrame/pandas.core.frame.DataFrame

Python Tutorials - Herong's Tutorial Examples

∟pandas - Data Analysis and Manipulation

∟pandas.DataFrame/pandas.core.frame.DataFrame - The Table Class

This section describes pandas.DataFrame/pandas.core.frame.DataFrame class, which represents a table of data with rows and columns.

What Is pandas.DataFrame/pandas.core.frame.DataFrame? pandas.DataFrame/pandas.core.frame.DataFrame is a class that represents a tabular structure with potentially heterogeneously-typed columns.

Main features of pandas.DataFrame are:

Columns are labeled - Columns can be accessed by the label or by the position. Column position number starts with 0.
Rows are indexed - Rows can be accessed by the index value or by the position. The default index value is the row position number which starts with 0.
Size is mutable - Rows and columns can dynamically added and removed.

Here are some basic properties, operations and methods provided in pandas.DataFrame class.

1. pd.DataFrame() - Method to construct a new DataFrame object. The example below uses a JSON string to provide input.

>>> import pandas as pd

>>>
df = pd.DataFrame({
  "Name": [
    "Braund, Mr. Owen Harris",
    "Allen, Mr. William Henry",
    "Bonnell, Miss. Elizabeth",
  ],
  "Age": [22, 35, 58],
  "Sex": ["male", "male", "female"],
})

>>> type(df)
<class 'pandas.core.frame.DataFrame'>

>>> print(df)
                       Name  Age     Sex
0   Braund, Mr. Owen Harris   22    male
1  Allen, Mr. William Henry   35    male
2  Bonnell, Miss. Elizabeth   58  female

2. df[col_name] or df.col_name - Operation to return a Series object representing data elements of the given column.

>>> age = df["Age"]
>>> age = df.Age

>>> type(age)

<class 'pandas.core.series.Series'>

>>> print(age)
0    22
1    35
2    58
Name: Age, dtype: int64

3. df[col_name][index], df.col_name[index] or df.at[index, col_name] - Operation to return the data element of the given index and the given column.

>>> a2 = df["Age"][2]
>>> a2 = df.Age[2]
>>> a2 = df.at[2, "Age"]

>>> type(a2)
<class 'numpy.int64'>

>>> print(a2)
58

4. df.iat[i, j] - Operation to return the data element of the given row position and the given column position of the DataFrame.

>>> a2 = df.iat[2, 1]

>>> type(a2)
<class 'numpy.int64'>

>>> print(a2)
58

5. df.columns - Property holding an Index object representing the column list.

>>> cols = df.columns

>>> type(cols)
<class 'pandas.core.indexes.base.Index'>

>>> print(cols)
Index(['Name', 'Age', 'Sex'], dtype='object')

6. df.shape - Property holding shape (number of rows and number of columns) of of the DataFrame.

>>> print(df.shape)
(3, 3)

7. df.append() - Method to append a Dict object as a row to the DataFrame.

>>> df.append({"Name": "John Smith", "Age": 18, "Sex": "male"})

8. df.groupby(column_list) - Method to return pandas.core.groupby.generic.DataFrameGroupBy object, which supports count() and other methods.

>>> grp = df.groupby(['Sex']).count()
        Name  Age
Sex
female  1     1
male    2     2

Table of Contents

About This Book

Running Python Code Online

Python on macOS Computers

Python on Linux Computers

Built-in Data Types

Variables, Operations and Expressions

Statements - Execution Units

Function Statement and Function Call

Iterators and Generators

List, Set and Dictionary Comprehensions

Classes and Instances

Modules and Module Files

Packages and Package Directories

"sys" and "os" Modules

"pathlib" - Object-Oriented Filesystem Paths

"pip" - Package Installer for Python

SciPy.org - Python Libraries for Science

►pandas - Data Analysis and Manipulation

What Is 'pandas'

►pandas.DataFrame/pandas.core.frame.DataFrame - The Table Class

pandas.core.series.Series - The Column Class

File Input and Output for DataFrame

Anaconda - Python Environment Manager

Jupyter Notebook and JupyterLab

References

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