How to Read Rows and Column in Python

March 10, 2022 Post a Comment

While working with the Python dataset Engineers clean the dataset every bit per the requirement of the project. Drib function is often used to remove rows & columns that might not be useful for the projection.

In this tutorial, nosotros will learn most python pandas driblet rows. Also, nosotros volition cover these topics.

Python Pandas Drop Function
Python pandas drop rows by alphabetize
Python pandas drib rows by status
Python pandas drop rows with nan in specific cavalcade
Python pandas drop rows with nan
Python pandas drop rows based on column value
Python pandas drop rows containing cord

The dataset we are using in this tutorial is downloaded from Kaggle.

Python Pandas Drop Function

Pandas drop is a function in Python pandas used to drop the rows or columns of the dataset. This part is often used in data cleaning. axis = 0 is referred every bit rows and axis = 1 is referred equally columns.

Syntax:

Here is the syntax for the implementation of the pandas drop()

          DataFrame.drop(     labels=None,      axis=0,      alphabetize=None,      columns=None,      level=None,      inplace=False,      errors='heighten' )

Options	Explanation
labels	Single label or list-like Index or Column labels to drop.
axis	the drop will remove provided centrality, the centrality can be 0 or one. centrality = 0 refers to rows or index (verticals) axis = 1 refers to columns (horizontals) by default, axis = 0
index	single characterization or list-like. the index is the row (verticals) & is equivalent to centrality=0
columns	Single label or list-like. the columns are horizontals in the tabular view & are denoted with centrality=ane.
level	int or level name, optional For MultiIndex, the level from which the labels will exist removed.
inplace	accepts bool (True or Simulated), default is False Inplace makes changes and so & in that location. don't need to assign a variable.
errors	the error tin exist 'ignored' or 'raised'. default is 'raised' if ignored suppress error and only existing labels are dropped if raised then information technology volition testify the error message & won't allow dropping the data.

As well read, How to employ Pandas drop() function in Python

Python pandas driblet rows by index

In this section, nosotros will learn how to drib rows past alphabetize in Python Pandas. To remove the rows past index all we have to practice is pass the index number or listing of index numbers in case of multiple drops.
to drib rows by alphabetize simply use this lawmaking: df.drib(alphabetize) . Here df is the dataframe on which y'all are working and in place of index type the index number or proper name.
Here is the implementation of lawmaking on the jupyter notebook please do read the comments and markdown for stride by step explanation.

Read How to convert floats to integer in Pandas

Python pandas drop rows by condition

In this section, we will acquire how to drop rows by status in Python pandas. So in that location could be a n-number of conditions that can exist practical depending upon the project.

Here is the implementation of dropping rows by status on jupyter notebook. Read comments and markdowns to empathise better.

Read, How to Drop Duplicates using drop_duplicates() part in Python Pandas

Python pandas drop rows with nan in specific column

In this section, we will learn how to drop rows with nan or missing value in specific cavalcade in Python pandas.
to remove missing values from the dataset dropna() role is used. Only to remove from a specific column that nosotros have to provide a subset value inside the dropna() function

Syntax:

Here is the syntax to remove missing values or nan from specific column(s)

          # remove from unmarried column df.drop(subset='column_name')  # remove from multiple columns df.drop(subset=['column1', column2, 'column3']

Hither is the implementation on the jupyter notebook. Please refer to comments & markdowns for step by step explanation.

Python pandas drop rows with nan

In this department, we will larn how to drop rows with nan. nan is an abbreviation of 'non a number' and is referred to missing values of the dataset.
dropna() function is used to drib all the missing values from the dataset in Python pandas.

Here is the implementation of drop rows with nan on jupyter notebook. Please read the comments and markdowns for footstep by pace explanation.

Read Python lexicon extend

Python pandas drop rows based on column value

In this section, nosotros will learn how to drop rows based on column value in Python Pandas. Here we can filter and remove the rows that do not match the criteria.

Here is the implementation on of drop rows based on column value on jupyter notebook.

Python pandas driblet rows containing string

In this section, we will larn how to drop rows containing string in Python Pandas. So will drop all the rows of the columns that incorporate datatype as a cord in information technology.
and so all we take to do here is filter numeric columns out and delete the remaining columns.
df.select_dtypes(exclude='number') this code snippet will return all the columns which do not have int equally datatype in it. If this command is passed in drop() so it will remove all the columns except those having numbers in it. So this is how we tin can drop cord rows.

Here is the implementation on jupyter notebook please refer to comments and markdown from step past step caption.

You lot may also like the following Python Pandas tutorials:

Missing Data in Pandas in Python
Python Pandas CSV Tutorial
Pandas DataFrame Iterrows
Pandas Delete Cavalcade
Crosstab in Python Pandas
Groupby in Python Pandas
How to Convert Pandas DataFrame to a Dictionary

In this tutorial, we have learned about python pandas drop rows. Too, we accept covered these topics.

python pandas drop rows by index
python pandas driblet rows by condition
python pandas drib rows with nan in specific column
python pandas drop rows with nan
python pandas drop rows based on column value
python pandas drib rows containing cord

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