Note

Go to the end to download the full example code. or to run this example in your browser via Binder

First level analysis of a complete BIDS dataset from openneuro

Full step-by-step example of fitting a GLM to perform a first level analysis in an openneuro BIDS dataset. We demonstrate how BIDS derivatives can be exploited to perform a simple one subject analysis with minimal code. Details about the BIDS standard are available at https://bids.neuroimaging.io/. We also demonstrate how to download individual groups of files from the Openneuro s3 bucket.

More specifically:

  1. Download an fMRI BIDS dataset with derivatives from openneuro.

  2. Extract first level model objects automatically from the BIDS dataset.

  3. Demonstrate Quality assurance of Nilearn estimation against available FSL. estimation in the openneuro dataset.

  4. Display contrast plot and uncorrected first level statistics table report.

Fetch openneuro BIDS dataset

We download one subject from the stopsignal task in the ds000030 V4 BIDS dataset available in openneuro. This dataset contains the necessary information to run a statistical analysis using Nilearn. The dataset also contains statistical results from a previous FSL analysis that we can employ for comparison with the Nilearn estimation.

from nilearn.datasets import (
    fetch_ds000030_urls,
    fetch_openneuro_dataset,
    select_from_index,
)

_, urls = fetch_ds000030_urls()

exclusion_patterns = [
    "*group*",
    "*phenotype*",
    "*mriqc*",
    "*parameter_plots*",
    "*physio_plots*",
    "*space-fsaverage*",
    "*space-T1w*",
    "*dwi*",
    "*beh*",
    "*task-bart*",
    "*task-rest*",
    "*task-scap*",
    "*task-task*",
]
urls = select_from_index(
    urls, exclusion_filters=exclusion_patterns, n_subjects=1
)

data_dir, _ = fetch_openneuro_dataset(urls=urls)

[fetch_ds000030_urls] Dataset found in
/home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed
[fetch_openneuro_dataset] Dataset found in
/home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed

Obtain FirstLevelModel objects automatically and fit arguments

From the dataset directory we automatically obtain FirstLevelModel objects with their subject_id filled from the BIDS dataset. Moreover we obtain, for each model, the list of run images and their respective events and confound regressors. Those are inferred from the confounds.tsv files available in the BIDS dataset. To get the first level models we have to specify the dataset directory, the task_label and the space_label as specified in the file names. We also have to provide the folder with the desired derivatives, that in this case were produced by the fMRIPrep BIDS app.

from nilearn.glm.first_level import first_level_from_bids

task_label = "stopsignal"
space_label = "MNI152NLin2009cAsym"
derivatives_folder = "derivatives/fmriprep"
(
    models,
    models_run_imgs,
    models_events,
    models_confounds,
) = first_level_from_bids(
    data_dir,
    task_label,
    space_label,
    smoothing_fwhm=5.0,
    derivatives_folder=derivatives_folder,
    n_jobs=2,
    verbose=1,
)

/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_bids_features.py:85: DeprecationWarning: Starting in version 0.12, slice_time_ref will default to None.
  ) = first_level_from_bids(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_bids_features.py:85: UserWarning:
No bold.json found in BIDS folder in:
 /home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed/derivatives/fmriprep.
  ) = first_level_from_bids(
[first_level_from_bids]
Found the following 1 preprocessed BOLD files
- for subject 10159
- for filter: [('task', 'stopsignal'), ('space', 'MNI152NLin2009cAsym')]:
        -
/home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed/derivatives/fmri
prep/sub-10159/func/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_desc-pre
proc_bold.nii.gz

[first_level_from_bids]
Found the following 1 events files
- for subject 10159
- for filter: [('task', 'stopsignal')]:
        -
/home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed/sub-10159/func/s
ub-10159_task-stopsignal_events.tsv

[first_level_from_bids]
Found the following 1 confounds files
- for subject 10159
- for filter: [('task', 'stopsignal')]:
        -
/home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed/derivatives/fmri
prep/sub-10159/func/sub-10159_task-stopsignal_desc-confounds_regressors.tsv

Access the model and model arguments of the subject and process events.

model, imgs, events, confounds = (
    models[0],
    models_run_imgs[0],
    models_events[0],
    models_confounds[0],
)
subject = f"sub-{model.subject_label}"
model.minimize_memory = False  # override default

from pathlib import Path

from nilearn.interfaces.fsl import get_design_from_fslmat

fsl_design_matrix_path = (
    Path(data_dir)
    / "derivatives"
    / "task"
    / subject
    / "stopsignal.feat"
    / "design.mat"
)
design_matrix = get_design_from_fslmat(
    fsl_design_matrix_path, column_names=None
)

We identify the columns of the Go and StopSuccess conditions of the design matrix inferred from the FSL file, to use them later for contrast definition.

design_columns = [
    f"cond_{int(i):02}" for i in range(len(design_matrix.columns))
]
design_columns[0] = "Go"
design_columns[4] = "StopSuccess"
design_matrix.columns = design_columns

First level model estimation (one subject)

We fit the first level model for one subject.

model.fit(imgs, design_matrices=[design_matrix])

[FirstLevelModel.fit] Loading data from
/home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed/derivatives/fmri
prep/sub-10159/func/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_desc-pre
proc_bold.nii.gz
[FirstLevelModel.fit] Computing the mask
[FirstLevelModel.fit] Resampling mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from
Nifti1Image('/home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed/der
ivatives/fmriprep/sub-10159/func/sub-10159_task-stopsignal_space-MNI152NLin2009c
Asym_desc-preproc_bold.nii.gz')
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 2 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 2 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 5 seconds.

First Level Model
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


Then we compute the StopSuccess - Go contrast. We can use the column names of the design matrix.

z_map = model.compute_contrast("StopSuccess - Go")

Visualize results

Let’s have a look at the Nilearn estimation and the FSL estimation available in the dataset.

import matplotlib.pyplot as plt
import nibabel as nib
from scipy.stats import norm

from nilearn.plotting import plot_glass_brain, show

fsl_z_map = nib.load(
    Path(data_dir)
    / "derivatives"
    / "task"
    / subject
    / "stopsignal.feat"
    / "stats"
    / "zstat12.nii.gz"
)

plot_glass_brain(
    z_map,
    threshold=norm.isf(0.001),
    title='Nilearn Z map of "StopSuccess - Go" (unc p<0.001)',
    plot_abs=False,
    display_mode="ortho",
)
plot_glass_brain(
    fsl_z_map,
    threshold=norm.isf(0.001),
    title='FSL Z map of "StopSuccess - Go" (unc p<0.001)',
    plot_abs=False,
    display_mode="ortho",
)
  • plot bids features
  • plot bids features
<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7f80fdaaa5b0>

We show the agreement between the 2 estimations.

from nilearn.plotting import plot_bland_altman, plot_img_comparison

plot_img_comparison(
    z_map, fsl_z_map, model.masker_, ref_label="Nilearn", src_label="FSL"
)

plot_bland_altman(
    z_map, fsl_z_map, model.masker_, ref_label="Nilearn", src_label="FSL"
)

show()
  • Pearson's R: 0.97, Histogram of imgs values
  • plot bids features
[NiftiMasker.wrapped] Loading data from Nifti1Image(
shape=(65, 77, 49, 1),
affine=array([[   3.,    0.,   -0.,  -96.],
       [   0.,    3.,   -0., -132.],
       [   0.,    0.,    4.,  -78.],
       [   0.,    0.,    0.,    1.]])
)
[NiftiMasker.wrapped] Smoothing images
[NiftiMasker.wrapped] Extracting region signals
[NiftiMasker.wrapped] Cleaning extracted signals
[NiftiMasker.wrapped] Loading data from Nifti1Image(
shape=(65, 77, 49, 1),
affine=array([[   3.,    0.,    0.,  -96.],
       [   0.,    3.,    0., -132.],
       [   0.,    0.,    4.,  -78.],
       [   0.,    0.,    0.,    1.]])
)
[NiftiMasker.wrapped] Smoothing images
[NiftiMasker.wrapped] Extracting region signals
[NiftiMasker.wrapped] Cleaning extracted signals
[NiftiMasker.wrapped] Loading data from Nifti1Image(
shape=(65, 77, 49, 1),
affine=array([[   3.,    0.,   -0.,  -96.],
       [   0.,    3.,   -0., -132.],
       [   0.,    0.,    4.,  -78.],
       [   0.,    0.,    0.,    1.]])
)
[NiftiMasker.wrapped] Smoothing images
[NiftiMasker.wrapped] Extracting region signals
[NiftiMasker.wrapped] Cleaning extracted signals
[NiftiMasker.wrapped] Loading data from Nifti1Image(
shape=(65, 77, 49, 1),
affine=array([[   3.,    0.,    0.,  -96.],
       [   0.,    3.,    0., -132.],
       [   0.,    0.,    4.,  -78.],
       [   0.,    0.,    0.,    1.]])
)
[NiftiMasker.wrapped] Smoothing images
[NiftiMasker.wrapped] Extracting region signals
[NiftiMasker.wrapped] Cleaning extracted signals

Saving model outputs to disk

We can now easily save the main results, the model metadata and an HTML report to the disk.

output_dir = Path.cwd() / "results" / "plot_bids_features"
output_dir.mkdir(exist_ok=True, parents=True)

from nilearn.interfaces.bids import save_glm_to_bids

stat_threshold = norm.isf(0.001)

save_glm_to_bids(
    model,
    contrasts="StopSuccess - Go",
    contrast_types={"StopSuccess - Go": "t"},
    out_dir=output_dir / "derivatives" / "nilearn_glm",
    threshold=stat_threshold,
    cluster_threshold=10,
)

/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_bids_features.py:210: UserWarning: Contrast name "StopSuccess - Go" changed to "stopsuccessMinusGo"
  save_glm_to_bids(
[save_glm_to_bids] Generating design matrices figures...
[save_glm_to_bids] Generating contrast matrices figures...
[save_glm_to_bids] Saving contrast-level statistical maps...
[save_glm_to_bids] Saving model level statistical maps...
[save_glm_to_bids] Generating HTML...

First Level Model
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


View the generated files

files = sorted((output_dir / "derivatives" / "nilearn_glm").glob("**/*"))
print("\n".join([str(x.relative_to(output_dir)) for x in files]))

derivatives/nilearn_glm/dataset_description.json
derivatives/nilearn_glm/sub-10159
derivatives/nilearn_glm/sub-10159/report.html
derivatives/nilearn_glm/sub-10159/statmap.json
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_clusters.json
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_clusters.tsv
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_design.png
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_stat-effect_statmap.nii.gz
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_stat-p_statmap.nii.gz
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_stat-t_statmap.nii.gz
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_stat-variance_statmap.nii.gz
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_stat-z_statmap.nii.gz
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_corrdesign.png
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_design.json
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_design.png
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_design.tsv
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_mask.nii.gz
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_stat-errorts_statmap.nii.gz
derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_stat-rsquared_statmap.nii.gz

Simple statistical report of thresholded contrast

We display the contrast plot and table with cluster information.

Here we will the image directly from the results saved to disk.

from nilearn.plotting import plot_contrast_matrix

plot_contrast_matrix("StopSuccess - Go", design_matrix)

z_map = (
    output_dir
    / "derivatives"
    / "nilearn_glm"
    / "sub-10159"
    / (
        "sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_"
        "contrast-stopsuccessMinusGo_stat-z_statmap.nii.gz"
    )
)

plot_glass_brain(
    z_map,
    threshold=stat_threshold,
    plot_abs=False,
    display_mode="z",
    figure=plt.figure(figsize=(4, 4)),
)
show()
  • plot bids features
  • plot bids features

The saved results include a table of activated clusters.

Note

This table can also be generated by using the function nilearn.reporting.get_clusters_table.

from nilearn.reporting import get_clusters_table

table = get_clusters_table(
    z_map,
    stat_threshold=norm.isf(0.001),
    cluster_threshold=10
)

See also

The restults saved to disk and the output of get_clusters_table do not contain the anatomical location of the clusters. To get the names of the location of the clusters according to one or several atlases, we recommend using the atlasreader package.

import pandas as pd

table_file = (
    output_dir
    / "derivatives"
    / "nilearn_glm"
    / "sub-10159"
    / (
        "sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_"
        "contrast-stopsuccessMinusGo_clusters.tsv"
    )
)

table = pd.read_csv(table_file, sep="\t")

table
Cluster ID X Y Z Peak Stat Cluster Size (mm3)
0 1 15.0 -93.0 -18.0 122.516815 27900.0
1 1a 6.0 -87.0 -18.0 70.342086 NaN
2 1b 30.0 -87.0 -22.0 69.308056 NaN
3 1c -3.0 -87.0 -18.0 61.796424 NaN
4 2 9.0 69.0 -6.0 92.734506 64908.0
... ... ... ... ... ... ...
78 34 -3.0 42.0 26.0 6.714833 468.0
79 34a -6.0 42.0 18.0 4.408391 NaN
80 35 -3.0 12.0 54.0 5.883419 396.0
81 35a 3.0 18.0 54.0 5.321565 NaN
82 36 -3.0 9.0 6.0 5.772909 468.0

83 rows × 6 columns



We can get a latex table from a Pandas Dataframe for display and publication purposes.

Note

This requires to have jinja2 installed:

pip install jinja2

print(table.to_latex())

\begin{tabular}{llrrrrr}
\toprule
 & Cluster ID & X & Y & Z & Peak Stat & Cluster Size (mm3) \\
\midrule
0 & 1 & 15.000000 & -93.000000 & -18.000000 & 122.516815 & 27900.000000 \\
1 & 1a & 6.000000 & -87.000000 & -18.000000 & 70.342086 & NaN \\
2 & 1b & 30.000000 & -87.000000 & -22.000000 & 69.308056 & NaN \\
3 & 1c & -3.000000 & -87.000000 & -18.000000 & 61.796424 & NaN \\
4 & 2 & 9.000000 & 69.000000 & -6.000000 & 92.734506 & 64908.000000 \\
5 & 2a & 12.000000 & 57.000000 & -22.000000 & 62.999189 & NaN \\
6 & 2b & -15.000000 & 15.000000 & 74.000000 & 54.897183 & NaN \\
7 & 2c & 15.000000 & 63.000000 & -10.000000 & 53.770578 & NaN \\
8 & 3 & 0.000000 & -3.000000 & -18.000000 & 80.143288 & 22788.000000 \\
9 & 3a & 27.000000 & 9.000000 & -26.000000 & 46.564125 & NaN \\
10 & 3b & 0.000000 & -15.000000 & -42.000000 & 40.157605 & NaN \\
11 & 3c & 3.000000 & -18.000000 & -50.000000 & 34.929461 & NaN \\
12 & 4 & -54.000000 & 42.000000 & 14.000000 & 36.477846 & 4896.000000 \\
13 & 4a & -45.000000 & 51.000000 & 22.000000 & 19.736986 & NaN \\
14 & 4b & -33.000000 & 51.000000 & 38.000000 & 17.196909 & NaN \\
15 & 4c & -27.000000 & 60.000000 & 34.000000 & 16.980278 & NaN \\
16 & 5 & -3.000000 & 21.000000 & -10.000000 & 26.752451 & 2736.000000 \\
17 & 5a & 0.000000 & 30.000000 & -6.000000 & 17.541399 & NaN \\
18 & 5b & -12.000000 & 18.000000 & -14.000000 & 9.857585 & NaN \\
19 & 5c & 3.000000 & 18.000000 & 2.000000 & 5.270521 & NaN \\
20 & 6 & -63.000000 & 9.000000 & 2.000000 & 24.590403 & 6552.000000 \\
21 & 6a & -57.000000 & 18.000000 & -6.000000 & 22.502379 & NaN \\
22 & 6b & -63.000000 & 27.000000 & 10.000000 & 21.651576 & NaN \\
23 & 6c & -63.000000 & -3.000000 & 6.000000 & 19.091516 & NaN \\
24 & 7 & 39.000000 & 6.000000 & 66.000000 & 24.417132 & 3096.000000 \\
25 & 7a & 51.000000 & 21.000000 & 50.000000 & 14.872285 & NaN \\
26 & 7b & 45.000000 & 6.000000 & 62.000000 & 14.719380 & NaN \\
27 & 7c & 51.000000 & 9.000000 & 54.000000 & 11.575969 & NaN \\
28 & 8 & 12.000000 & 36.000000 & -26.000000 & 20.716392 & 396.000000 \\
29 & 8a & 18.000000 & 30.000000 & -26.000000 & 14.789520 & NaN \\
30 & 9 & -45.000000 & 6.000000 & 62.000000 & 20.216765 & 576.000000 \\
31 & 9a & -51.000000 & 9.000000 & 54.000000 & 17.445403 & NaN \\
32 & 10 & -39.000000 & -60.000000 & 62.000000 & 18.839163 & 1224.000000 \\
33 & 10a & -48.000000 & -48.000000 & 62.000000 & 17.127426 & NaN \\
34 & 10b & -30.000000 & -63.000000 & 66.000000 & 7.893538 & NaN \\
35 & 10c & -57.000000 & -33.000000 & 58.000000 & 7.533043 & NaN \\
36 & 11 & -3.000000 & -87.000000 & 46.000000 & 18.639460 & 3132.000000 \\
37 & 11a & -39.000000 & -75.000000 & 50.000000 & 15.066584 & NaN \\
38 & 11b & -18.000000 & -81.000000 & 54.000000 & 14.665675 & NaN \\
39 & 11c & -27.000000 & -81.000000 & 50.000000 & 10.536638 & NaN \\
40 & 12 & 12.000000 & -105.000000 & -2.000000 & 18.465759 & 864.000000 \\
41 & 12a & 12.000000 & -93.000000 & -2.000000 & 6.515125 & NaN \\
42 & 12b & 21.000000 & -105.000000 & -2.000000 & 5.758570 & NaN \\
43 & 13 & 21.000000 & -93.000000 & 34.000000 & 17.517443 & 792.000000 \\
44 & 14 & 63.000000 & 12.000000 & 10.000000 & 16.758346 & 828.000000 \\
45 & 14a & 63.000000 & 21.000000 & 14.000000 & 8.923981 & NaN \\
46 & 14b & 60.000000 & 12.000000 & -2.000000 & 7.959472 & NaN \\
47 & 15 & -18.000000 & -60.000000 & -58.000000 & 16.093286 & 504.000000 \\
48 & 15a & -18.000000 & -72.000000 & -58.000000 & 6.096305 & NaN \\
49 & 16 & -9.000000 & 33.000000 & -26.000000 & 15.756994 & 900.000000 \\
50 & 16a & -9.000000 & 42.000000 & -22.000000 & 11.028668 & NaN \\
51 & 17 & -15.000000 & -108.000000 & -2.000000 & 14.652072 & 936.000000 \\
52 & 18 & -9.000000 & -48.000000 & 78.000000 & 13.593056 & 720.000000 \\
53 & 18a & -6.000000 & -57.000000 & 74.000000 & 7.156604 & NaN \\
54 & 19 & 18.000000 & 18.000000 & 70.000000 & 13.349184 & 468.000000 \\
55 & 20 & -27.000000 & -93.000000 & 30.000000 & 13.055884 & 540.000000 \\
56 & 21 & 57.000000 & -51.000000 & -14.000000 & 12.623032 & 612.000000 \\
57 & 22 & 27.000000 & -78.000000 & 50.000000 & 12.179269 & 612.000000 \\
58 & 22a & 36.000000 & -75.000000 & 50.000000 & 6.740345 & NaN \\
59 & 23 & -30.000000 & -102.000000 & 6.000000 & 11.286718 & 360.000000 \\
60 & 24 & 63.000000 & -12.000000 & 10.000000 & 11.197878 & 648.000000 \\
61 & 24a & 54.000000 & -15.000000 & 10.000000 & 5.924582 & NaN \\
62 & 25 & -69.000000 & -30.000000 & -10.000000 & 11.079066 & 468.000000 \\
63 & 25a & -69.000000 & -30.000000 & 2.000000 & 6.227700 & NaN \\
64 & 26 & 63.000000 & 0.000000 & 26.000000 & 9.853608 & 576.000000 \\
65 & 26a & 66.000000 & -3.000000 & 34.000000 & 7.613071 & NaN \\
66 & 27 & -66.000000 & 9.000000 & 18.000000 & 9.502274 & 1296.000000 \\
67 & 27a & -66.000000 & 3.000000 & 26.000000 & 7.890005 & NaN \\
68 & 27b & -60.000000 & -3.000000 & 26.000000 & 7.157832 & NaN \\
69 & 27c & -66.000000 & -6.000000 & 14.000000 & 5.843833 & NaN \\
70 & 28 & 30.000000 & -99.000000 & 10.000000 & 8.887969 & 360.000000 \\
71 & 28a & 33.000000 & -99.000000 & 2.000000 & 7.337295 & NaN \\
72 & 29 & -66.000000 & -18.000000 & 42.000000 & 8.635179 & 468.000000 \\
73 & 30 & -48.000000 & 21.000000 & 50.000000 & 8.539273 & 396.000000 \\
74 & 31 & -36.000000 & -36.000000 & -22.000000 & 8.397082 & 396.000000 \\
75 & 31a & -27.000000 & -27.000000 & -26.000000 & 4.379355 & NaN \\
76 & 32 & -45.000000 & 6.000000 & 42.000000 & 7.232974 & 396.000000 \\
77 & 33 & -15.000000 & -57.000000 & -10.000000 & 7.195093 & 396.000000 \\
78 & 34 & -3.000000 & 42.000000 & 26.000000 & 6.714833 & 468.000000 \\
79 & 34a & -6.000000 & 42.000000 & 18.000000 & 4.408391 & NaN \\
80 & 35 & -3.000000 & 12.000000 & 54.000000 & 5.883419 & 396.000000 \\
81 & 35a & 3.000000 & 18.000000 & 54.000000 & 5.321565 & NaN \\
82 & 36 & -3.000000 & 9.000000 & 6.000000 & 5.772909 & 468.000000 \\
\bottomrule
\end{tabular}

You can also print the output to markdown, if you have the tabulate dependencies installed.

pip install tabulate

table.to_markdown()

Total running time of the script: (0 minutes 38.420 seconds)

Estimated memory usage: 1006 MB

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