<jupyter_start><jupyter_text>Data frames manipulation with pandas [pandas](https://pandas.pydata.org/) -  fast, powerful, flexible and easy to use open source data analysis and manipulation tool,built on top of the Python programming language.----------------<jupyter_code>## Load libraries
import pandas as pd
import matplotlib.pyplot as plt<jupyter_output><empty_output><jupyter_text>1. Load & plot confounds variables **Confounds** (or *nuisance regressors*) are variables representing fluctuations with a potential non-neuronal origin. Confounding variables calculated by [fMRIPrep](https://fmriprep.readthedocs.io/en/stable/outputs.htmlconfounds) are stored separately for each subject, session and run in TSV files - one column for each confound variable.<jupyter_code># Load data

confounds_path = "data/sub-01_ses-1_task-rest_bold_confounds.tsv"


# Print first 5 rows of data

# Print column names


# Plot mean timeseries from cerebrospinal fluid (CSF) and white matter 


# Plot cerebrospinal fluid (CSF) and white matter timeseries on a scatterplot


# Plot cerebrospinal fluid (CSF) and white matter timeseries on a scatterplot (use seaborn joint plot)
import seaborn as sns<jupyter_output><empty_output><jupyter_text>2. Load and plot COVID-19 dataDownload data:[COVID-19](http://shinyapps.org/apps/corona/) (check out their GH)<jupyter_code># Load some COVID-19 data

# covid_path = 


# Print first 5 rows of data


# Group by country & sum cases


# Filter dataframe by cases in Poland


# Plot cases in Poland


# Plot cases in other country


# Plot cases of multiple countries on a one plot<jupyter_output><empty_output>
StarcoderdataJupyterStructuredCleanDedup