Python series column name

pandas.Series.name#

The name of a Series becomes its index or column name if it is used to form a DataFrame. It is also used whenever displaying the Series using the interpreter.

Returns : label (hashable object)

The name of the Series, also the column name if part of a DataFrame.

Sets the Series name when given a scalar input.

Corresponding Index property.

The Series name can be set initially when calling the constructor.

>>> s = pd.Series([1, 2, 3], dtype=np.int64, name='Numbers') >>> s 0 1 1 2 2 3 Name: Numbers, dtype: int64 >>> s.name = "Integers" >>> s 0 1 1 2 2 3 Name: Integers, dtype: int64 

The name of a Series within a DataFrame is its column name.

>>> df = pd.DataFrame([[1, 2], [3, 4], [5, 6]], . columns=["Odd Numbers", "Even Numbers"]) >>> df Odd Numbers Even Numbers 0 1 2 1 3 4 2 5 6 >>> df["Even Numbers"].name 'Even Numbers' 

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How to Easily Name Pandas Series Columns in Python

Learn how to assign column names to a pandas series in Python using the `rename()` function, the `columns` attribute, and the `name` attribute of a Series. Optimize pandas code for performance and memory usage with these tips.

• Understanding Pandas DataFrames and Series
• Renaming Columns in a Pandas DataFrame
• How to Change COLUMN NAMES or Rename cols in DataFrame
• Setting Column Names Using DataFrame Columns Attribute
• Using Series Name Attribute to Assign Column Names
• Optimizing Pandas Code for Performance and Memory Usage
• Other quick code samples for assigning column names to a pandas series in Python
• Conclusion
• How do I give a column name in pandas DataFrame?
• How do you name columns in a data frame?
• How do you pass a column name as a parameter in Python?
• How do I store column names in a list Python?

Pandas is a popular Python library used for data analysis and manipulation. It provides powerful tools for data cleaning, transformation , and analysis. A pandas DataFrame is a collection of series, where each series represents a column. In this article, we will learn about how to assign column names to a pandas series in Python.

Understanding Pandas DataFrames and Series

Before we dive into assigning column names to pandas series, let’s first understand what are pandas dataframes and series .

A Pandas DataFrame is a two-dimensional table with labeled axes. It is similar to a spreadsheet or a SQL table. Each column in a DataFrame is a series object. Series are one- dimensional labeled arrays capable of holding data of any type. Each series in a DataFrame represents a column and takes its name from the series name.

To get the column names of a pandas DataFrame, we can use the DataFrame.columns.values attribute. Here’s an example:

import pandas as pddf = pd.DataFrame('A': [1, 2], 'B': [3, 4]>) print(df.columns.values) 

Renaming Columns in a Pandas DataFrame

There are several ways to rename columns in a pandas DataFrame. We can use the rename() function to rename columns, indexes, and axis. Here’s an example:

import pandas as pddf = pd.DataFrame('A': [1, 2], 'B': [3, 4]>) df.rename(columns='A': 'new_name_A', 'B': 'new_name_B'>, inplace=True) print(df.columns.values) 

In the above example, we used the rename() function to rename columns ‘A’ and ‘B’ to ’new_name_A’ and ’new_name_B’ respectively. We passed the dictionary to the columns parameter of the rename() function. The inplace=True parameter modifies the original DataFrame.

We can also change the case of column names using the rename() function. Here’s an example:

import pandas as pddf = pd.DataFrame('A': [1, 2], 'B': [3, 4]>) df.rename(columns=str.lower, inplace=True) print(df.columns.values) 

In the above example, we used the str.lower function to change the case of column names to lowercase. We passed the function to the columns parameter of the rename() function. The inplace=True parameter modifies the original DataFrame.

Another way to rename columns in a pandas DataFrame is by setting the columns attribute of the DataFrame. Here’s an example:

import pandas as pddf = pd.DataFrame('A': [1, 2], 'B': [3, 4]>) df.columns = ['new_name_A', 'new_name_B'] print(df.columns.values) 

In the above example, we set the columns attribute of the DataFrame to [‘new_name_A’, ‘new_name_B’] . This sets the column names of the DataFrame.

How to Change COLUMN NAMES or Rename cols in DataFrame

Setting Column Names Using DataFrame Columns Attribute

We can also set column names using the columns attribute of a DataFrame. Here’s an example:

import pandas as pddf = pd.DataFrame('A': [1, 2], 'B': [3, 4]>) df.columns = ['new_name_A', 'new_name_B'] print(df.columns.tolist()) 

In the above example, we set the columns attribute of the DataFrame to [‘new_name_A’, ‘new_name_B’] . We then used the tolist() method to convert the column names to a list.

Using Series Name Attribute to Assign Column Names

The name attribute of a Series becomes its index or column name if it is used to form a DataFrame. Here’s an example:

import pandas as pds = pd.Series([1, 2], name='new_name_A') df = pd.DataFrame(s) print(df.columns.values) 

In the above example, we created a pandas Series s with the name attribute set to ‘new_name_A’ . We then created a DataFrame df with the Series s . The column name of the DataFrame is ‘new_name_A’ .

We can also add a prefix or suffix to column names using the add_prefix() and add_suffix() methods. Here’s an example:

import pandas as pddf = pd.DataFrame('A': [1, 2], 'B': [3, 4]>) df = df.add_prefix('prefix_') df = df.add_suffix('_suffix') print(df.columns.values) 

['prefix_A_suffix' 'prefix_B_suffix'] 

In the above example, we used the add_prefix() and add_suffix() methods to add a prefix ‘prefix_’ and a suffix ‘_suffix’ to the column names of the DataFrame.

Optimizing Pandas Code for Performance and Memory Usage

Pandas is a powerful library, but it can also be slow and memory-intensive if not used correctly. Here are some tips for Optimizing Pandas Code :

• Use vectorized operations whenever possible. Vectorized operations are faster than for loops because they can take advantage of hardware acceleration.
• Avoid creating unnecessary copies of data. Data copies can take up a lot of memory and slow down your code.
• Use the apply() function sparingly. The apply() function is slow because it applies a function to each row or column of a DataFrame.
• Use the dtype parameter to specify the data type of columns. This can save memory and improve performance.

Other quick code samples for assigning column names to a pandas series in Python

In Python , for example, name columns pandas

df.columns = ['column1', 'column2', 'column3'] df.columns

In Python , for instance, name columns pandas code sample

>gapminder.columns = ['country','year','population', 'continent','life_exp','gdp_per_cap'] 

Conclusion

In conclusion, assigning column names to a pandas series in Python involves using the rename() function, the columns attribute, and the name attribute of a Series. renaming columns in a pandas dataframe can be done using the rename() function. Optimizing pandas code can improve performance and memory usage . By following these steps and best practices, users can effectively assign column names to a pandas series in Python.

Frequently Asked Questions — FAQs

How do I rename a single column in a Pandas DataFrame?

You can use the `rename()` function and pass a dictionary with the old column name as the key and the new column name as the value. For example: `df.rename(columns=<'old_name': 'new_name'>, inplace=True)`.

Can I set column names using a list in Pandas?

Yes, you can set column names using a list in Pandas. For example: `df.columns = [‘new_name1’, ‘new_name2’]`.

How do I add a prefix or suffix to column names in a Pandas DataFrame?

You can use the `add_prefix()` or `add_suffix()` function. For example: `df = df.add_prefix(‘prefix_’)` or `df = df.add_suffix(‘_suffix’)`.

How can I optimize my pandas code for better performance and memory usage?

Some tips for optimizing pandas code include using vectorized operations, avoiding for loops, and using the `apply()` function sparingly. It’s also important to avoid creating unnecessary copies of data.

How do I get a list of column names in a Pandas DataFrame?

Can I assign column names to a Pandas Series before creating a DataFrame?

Yes, you can assign column names to a Pandas Series using the `name` attribute. For example: `my_series.name = ‘my_column_name’`.

Источник

pandas.Series.name#

The name of a Series becomes its index or column name if it is used to form a DataFrame. It is also used whenever displaying the Series using the interpreter.

Returns : label (hashable object)

The name of the Series, also the column name if part of a DataFrame.

Sets the Series name when given a scalar input.

Corresponding Index property.

The Series name can be set initially when calling the constructor.

>>> s = pd.Series([1, 2, 3], dtype=np.int64, name='Numbers') >>> s 0 1 1 2 2 3 Name: Numbers, dtype: int64 >>> s.name = "Integers" >>> s 0 1 1 2 2 3 Name: Integers, dtype: int64 

The name of a Series within a DataFrame is its column name.

>>> df = pd.DataFrame([[1, 2], [3, 4], [5, 6]], . columns=["Odd Numbers", "Even Numbers"]) >>> df Odd Numbers Even Numbers 0 1 2 1 3 4 2 5 6 >>> df["Even Numbers"].name 'Even Numbers' 

Источник

pandas.Series.name#

The name of a Series becomes its index or column name if it is used to form a DataFrame. It is also used whenever displaying the Series using the interpreter.

Returns label (hashable object)

The name of the Series, also the column name if part of a DataFrame.

Sets the Series name when given a scalar input.

Corresponding Index property.

The Series name can be set initially when calling the constructor.

>>> s = pd.Series([1, 2, 3], dtype=np.int64, name='Numbers') >>> s 0 1 1 2 2 3 Name: Numbers, dtype: int64 >>> s.name = "Integers" >>> s 0 1 1 2 2 3 Name: Integers, dtype: int64 

The name of a Series within a DataFrame is its column name.

>>> df = pd.DataFrame([[1, 2], [3, 4], [5, 6]], . columns=["Odd Numbers", "Even Numbers"]) >>> df Odd Numbers Even Numbers 0 1 2 1 3 4 2 5 6 >>> df["Even Numbers"].name 'Even Numbers' 

Источник