Python Converting Statsmodels Summary Object To Pandas Dataframe

Leo Migdal

-Dec 4, 2025, 8:26 AM

python converting statsmodels summary object to pandas dataframe

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Find centralized, trusted content and collaborate around the technologies you use most. Bring the best of human thought and AI automation together at your work. This very simple case-study is designed to get you up-and-running quickly with statsmodels. Starting from raw data, we will show the steps needed to estimate a statistical model and to draw a diagnostic plot. We will only use functions provided by statsmodels or its pandas and patsy dependencies. After installing statsmodels and its dependencies, we load a few modules and functions:

pandas builds on numpy arrays to provide rich data structures and data analysis tools. The pandas.DataFrame function provides labelled arrays of (potentially heterogenous) data, similar to the R “data.frame”. The pandas.read_csv function can be used to convert a comma-separated values file to a DataFrame object. patsy is a Python library for describing statistical models and building Design Matrices using R-like formulas. This example uses the API interface. See Import Paths and Structure for information on the difference between importing the API interfaces (statsmodels.api and statsmodels.tsa.api) and directly importing from the module that defines the model.

We can then read any of those formats back as a pd.DataFrame: import statsmodels.api as sm model = sm.OLS (y,x) results = model.fit () results_summary = results.summary () # Note that tables is a... The table at index 1 is the “core” table. The model is estimated using ordinary least squares regression (OLS). To fit most of the models covered by statsmodels, you will need to create two design matrices. The first is a matrix of endogenous variable (s) (i.e. dependent, response, regressand, etc.).

The answer from @Michael B works well, but requires “recreating” the table. The table itself is actually directly available from the summary ().tables attribute. Each table in this attribute (which is a list of tables) is a SimpleTable, which has methods for outputting different formats. What do you need to know about Statsmodels in Python? Statsmodels is a Python module which provides various functions for estimating different statistical models and performing statistical tests First, we define the set of dependent ( y) and independent ( X) variables. If the dependent variable is in non-numeric form, it is first converted to numeric using dummies.

When dealing with data analysis and statistical modeling in Python, two powerful libraries often shine: pandas and statsmodels. Pandas, with its robust data manipulation capabilities, can handle large datasets efficiently, while statsmodels offers statistical tests and data exploration capabilities. The combination of these two libraries can significantly enhance your data manipulation skills and expand your analytical toolset. Before we dive into integration tactics, it is crucial to understand the individual functionalities of both libraries. First, ensure you have both libraries installed in your environment: Let's quickly load the libraries in Python:

Pandas can read and write diverse data formats like CSV, Excel, SQL databases, and more. For example, to read data from a CSV file: Now, you have a DataFrame named data that you can manipulate, summarize, and transform. Pandas, an incredibly versatile data manipulation library for Python, has various capabilities to calculate summary statistics on datasets. Summary statistics can give you a fast and comprehensive overview of the most important features of a dataset. In the following article, we will explore five methods of computing summary statistics using Pandas.

The describe() method is a strong method to generate descriptive statistics of a DataFrame. The describe() method will provide you with detailed summary statistics including count, mean, standard deviation, min, 25th percentile, median (50th percentile), 75th percentile, and max. Explanation: In this, we create a dictionary with numerical values and convert it to a Pandas Dataframe. We then used the describe() function on the DataFrame which provides a summary of key statistics including count, mean, standard deviation and percentiles. Pandas also has distinct functions to calculate the mean, median and mode of each column in a DataFrame. Explanation: mean() function calculates the average of each column, median() finds the middle value when sorted and mode() identifies the most frequent value.

Since mode() returns a DataFrame, we extract the first row using .iloc[0]. I am doing multiple linear regression with statsmodels.formula.api (ver 0.9.0) on Windows 10. After fitting the model and getting the summary with following lines i get summary in summary object format. I want to do backward elimination for P values for significance level 0.05. For this i need to remove the predictor with highest P values and run the code again. I wanted to know if there is a way to extract the P values from the summary object, so that i can run a loop with conditional statement and find the significant variables without...

The answer from @Michael B works well, but requires "recreating" the table. The table itself is actually directly available from the summary().tables attribute. Each table in this attribute (which is a list of tables) is a SimpleTable, which has methods for outputting different formats. We can then read any of those formats back as a pd.DataFrame: 问题:如何重塑熊猫。系列在我看来,它就像 pandas.Series 中的一个错误。 a = pd.Series([1,2,3,4]) b = a.reshape(2,2) b b 有类型 Series 但无法显示,最后一条语句给出异常,非常冗长,最后一行是“TypeError: %d format: a number is required, not numpy.ndarray”。 b.sha I’ve built dozens of regression models over the years, and here’s what I’ve learned: the math behind linear regression is straightforward, but getting it right requires understanding what’s happening under the hood.

That’s where statsmodels shines. Unlike scikit-learn, which optimizes for prediction, statsmodels gives you the statistical framework to understand relationships in your data. Let’s work through linear regression in Python using statsmodels, from basic implementation to diagnostics that actually matter. Statsmodels is a Python library that provides tools for estimating statistical models, including ordinary least squares (OLS), weighted least squares (WLS), and generalized least squares (GLS). Think of it as the statistical counterpart to scikit-learn. Where scikit-learn focuses on prediction accuracy, statsmodels focuses on inference: understanding which variables matter, quantifying uncertainty, and validating assumptions.

The library gives you detailed statistical output including p-values, confidence intervals, and diagnostic tests. This matters when you’re not just predicting house prices but explaining to stakeholders why square footage matters more than the number of bathrooms. Start with the simplest case: one predictor variable. Here’s a complete example using car data to predict fuel efficiency:

Python Converting Statsmodels Summary Object To Pandas Dataframe

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Pandas Builds On Numpy Arrays To Provide Rich Data Structures

We Can Then Read Any Of Those Formats Back As

The Answer From @Michael B Works Well, But Requires “recreating”