install.packages("GGally", repos='http://cran.us.r-project.org')
df.columns.tolist()
conda install -c bioconda os
df = pd.read_csv('file.tar.gz', compression='gzip', header=0, sep='\t', quotechar='"', error_bad_lines=False)
pd.read_csv(file, low_memory=False,usecols=lambda c: not c.startswith('Unnamed:'))
usecols=lambda c: not c.startswith('Unnamed:')
df['Condition'] == "After food"
plt.scatter(outputs[:,0], outputs[:,1], s=1, c=y_train, cmap='rainbow')
data = df.corr() sns.heatmap(data.corr(), annot=True) plt.tight_layout() plt.show()
plt.subplots(figsize=(25, 50)) count = 1 for i in range(len(num_data.columns)-1): plt.subplot(14, 4, count) sns.scatterplot(x=num_data.columns[i], y=num_data.columns[i+1], data=num_data, color='red') count += 1 plt.show()
ncols=5 length = corr_df.shape[1] nrows=length//ncols if length%ncols==0 else length//ncols+1
fig, ax = plt.subplots(nrows=nrows, ncols = ncols, figsize = (22, 12))
for idx, col in enumerate(corr_df):
i=idx//ncols
j=idx%ncols
sns.regplot(x = 'price', y = col, data = corr_df, ax = ax[i][j])
plt.tight_layout()
df.describe(include='object')
#!pip uninstall umap #!pip install umap-learn import umap.umap_ as umap
!pip install holoviews
!pip install umap-learn[plot] !pip holoviews==1.12.7 !pip install -U ipykernel
!git clone https://github.com/lmcinnes/umap !cd umap !pip install --user -r requirements.txt !python setup.py install --user
!pip install 'umap-learn==0.3.10'