Tony Shouse redpoint13

I had many driver installed I my virtual machine , so It was actually the reason why I was having the error.

To fix it I had first to remove all driver I have installed before using :

After that I when a head and installed the latest version of it nvidia driver:

	sudo apt-get update && \
	sudo apt-get install build-essential software-properties-common -y && \
	sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y && \
	sudo apt-get update && \
	sudo apt-get install gcc-6 g++-6 -y && \
	sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-6 60 --slave /usr/bin/g++ g++ /usr/bin/g++-6 && \
	gcc -v

	import seaborn as sns
	cols = df.columns[:30] # first 30 columns
	colors = ['#000099', '#ffff00'] # specify the colours - yellow is missing. blue is not missing.
	sns.heatmap(df[cols].isnull(), cmap=sns.color_palette(colors))

	# if it's a larger dataset and the visualization takes too long can do this.
	# % of missing.
	for col in df.columns:
	pct_missing = np.mean(df[col].isnull())
	if pct_missing > 0.009:

	# variance_inflation_factor
	# VIF >= 5 indicates issue w multicollearity

	from statsmodels.stats.outliers_influence import variance_inflation_factor
	from statsmodels.tools.tools import add_constant

	X = add_constant(data.select_dtypes(include=np.number).fillna(-1))
	vif = pd.Series([variance_inflation_factor(X.values, i) for i in range(X.shape[1])], index=X.columns)
	vif

	def clean_df_chars(df):
	"""Clean non-printable characters from headers and data."""
	df.columns.str.replace("/[^ -~]+/g", "").str.strip()
	df = df.replace(to_replace="/[^ -~]+/g", value="", regex=True)
	return df

	# True Positive (TP): we predict a label of 1 (positive), and the true label is 1.
	TP = np.sum(np.logical_and(pred_labels == 1, true_labels == 1))

	# True Negative (TN): we predict a label of 0 (negative), and the true label is 0.
	TN = np.sum(np.logical_and(pred_labels == 0, true_labels == 0))

	# False Positive (FP): we predict a label of 1 (positive), but the true label is 0.
	FP = np.sum(np.logical_and(pred_labels == 1, true_labels == 0))

	# False Negative (FN): we predict a label of 0 (negative), but the true label is 1.

	import filecmp
	c = filecmp.dircmp(filepath1, filepath2)

	def report_recursive(dcmp):
	for name in dcmp.diff_files:
	print("DIFF file %s found in %s and %s" % (name,
	dcmp.left, dcmp.right))
	for name in dcmp.left_only:
	print("ONLY LEFT file %s found in %s" % (name, dcmp.left))
	for name in dcmp.right_only:

	for c in cols_cat:
	df = pd.concat([df, pd.get_dummies(df[c], prefix=c,dummy_na=True)], axis=1).drop([c], axis=1)

	# List unique values in a DataFrame column
	# h/t @makmanalp for the updated syntax!
	df['Column Name'].unique()

	# Convert Series datatype to numeric (will error if column has non-numeric values)
	# h/t @makmanalp
	pd.to_numeric(df['Column Name'])

	# Convert Series datatype to numeric, changing non-numeric values to NaN
	# h/t @makmanalp for the updated syntax!