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Can't get correct input for DBSCAN clustersing

I have a node2vec embedding stored as a .csv file, values are a square symmetric matrix. I have two versions of this, one with node names in the first column and another with node names in the first row. I would like to cluster this data with DBSCAN, but I can't seem to figure out how to get the input right. I tried this:
import numpy as np
import pandas as pd
from sklearn.cluster import DBSCAN
from sklearn import metrics
input_file = "node2vec-labels-on-columns.emb"
# for tab delimited use:
df = pd.read_csv(input_file, header = 0, delimiter = "\t")
# put the original column names in a python list
original_headers = list(df.columns.values)
emb = df.as_matrix()
db = DBSCAN(eps=0.3, min_samples=10).fit(emb)
labels = db.labels_
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
n_noise_ = list(labels).count(-1)
print("Estimated number of clusters: %d" % n_clusters_)
print("Estimated number of noise points: %d" % n_noise_)
This leads to an error:
dbscan.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
emb = df.as_matrix()
Traceback (most recent call last):
File "dbscan.py", line 15, in <module>
db = DBSCAN(eps=0.3, min_samples=10).fit(emb)
File "C:\Python36\lib\site-packages\sklearn\cluster\_dbscan.py", line 312, in fit
X = self._validate_data(X, accept_sparse='csr')
File "C:\Python36\lib\site-packages\sklearn\base.py", line 420, in _validate_data
X = check_array(X, **check_params)
File "C:\Python36\lib\site-packages\sklearn\utils\validation.py", line 73, in inner_f
return f(**kwargs)
File "C:\Python36\lib\site-packages\sklearn\utils\validation.py", line 646, in check_array
allow_nan=force_all_finite == 'allow-nan')
File "C:\Python36\lib\site-packages\sklearn\utils\validation.py", line 100, in _assert_all_finite
msg_dtype if msg_dtype is not None else X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
I've tried other input methods that lead to the same error. All the tutorials I can find use datasets imported form sklearn so those are of not help figuring out how to read from a file. Can anyone point me in the right direction?

The error does not come from the fact that you are reading the dataset from a file but on the content of the dataset.
DBSCAN is meant to be used on numerical data. As stated in the error, it does not support NaNs.
If you are willing to cluster strings or labels, you should find some other model.

Confuse why my KNN code is throwing a ValueError

I am using sklearn for KNN regressor:
#importing libraries and data
import pandas as pd
from sklearn.neighbors import KNeighborsRegressor as KNR
theta = pd.read_csv("train.csv")#pandas dataframe
#getting data wanted from theta and putting it in a new dataframe
a = theta.get("YearBuilt")
b = theta.get("YrSold")
A = a.to_frame()
B = b.to_frame()
glasses = [A,B]
x = pd.concat(glasses)
#getting target data
y = theta.get("SalePrice")
#using KNN
horses = KNR(n_neighbors = 3)
horses.fit(x,y)
I get this error message:
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
Could someone please explain this? My data is in the hundred thousands for target and the thousands for input. And there is no blanks in the data.

Before answering the question, Let me refactor the code. You are using a dataframe so you can index single or muliple fields of the dataframe without going through the extra steps you've used:
#importing libraries and data
import pandas as pd
from sklearn.neighbors import KNeighborsRegressor as KNR
theta = pd.read_csv("train.csv") # pandas dataframe
#getting data wanted from theta and putting it in a new dataframe
x = theta[["YearBuilt", "YrSold"]] # index multiple fields
#getting target data
y = theta["SalePrice"] # index single field
#using KNN
horses = KNR(n_neighbors = 3)
horses.fit(x,y) # fit KNN
Regarding your error, it indicates that you have some NaN, Inf, large values in your data. You can ensure these doesnt occur by filtering out the NaN and inf values using this:
theta = theta.replace([np.inf, -np.inf], np.nan)
theta.dropna(inplace=True)

Data imputation in Python for Google Analytics data

I have sets of Google Analytics data from a website which I plan to analyse for a project. However, due to maintenance and other factors, there are chunks of dates for which there is no data. I want to impute this data while still maintaining the integrity of the data as I plan to plot these sets and compare the curves of different sets to each-other over time.
Example
I want to use the nearest valid datapoints to each missing datapoint to impute that value in order to maintain the underlying shape that can be seen from the image.
I've already tried to use scikit-learn's KNN-Imputer and Iterative Imputer but I'm either miss-understanding how these imputers are supposed to be used or they're not the correct for what I'm trying to do, potentially both.
import pandas as pd
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
import numpy as np
df = pd.read_csv('data.csv', names=['Day','Views'],delimiter=',',skiprows=3, usecols=[0,1], skipfooter=1, engine='python', quoting= 1)
df = df.replace(0, np.nan)
da = df.Views.rename_axis('ID').values
da = da.reshape(-1,1)
imputer = IterativeImputer(n_nearest_features = 100, max_iter = 10)
df_imputed = imputer.fit_transform(da)
df_imputed.reshape(1,-1)
df.Views = df_imputed
df
All of the NaN values are calculated to be the exact same number from what I have currently implemented.
Any help would be greatly appreciated.

The problem here was I reshaping the array. My data was just a 1D array of values so I was making it 2D by reshaping the array which was causing all the NaN values to be calculated as the same. When I added an index column and included this as an input to the imputer the values were calculated correctly.I also ended up using a KNN imputer from sklearn instead of the iterative imputer in this instance.

Issue with Scikit-learn data analysis

am attempting to take a .dat file of about 90,000 data lines of two variables (wavelength and intensity) and apply a sklearn.pca filter to it.
Here is a small set of that data:
wavelength intensity
[um] [W/m**2/um/sr]
196.078431372549 1.108370393265022E-003
192.307692307692 1.163428008597600E-003
188.679245283019 1.223639983609668E-003
The code I am using to analyze the data is below
pca= PCA(n_components=2)
pca.fit(data)
print(pca.components_)
The error code I get is this when I try to apply 2 pca components to one of the data sets:
ValueError: Datatype coercion is not allowed
Any help resolving would be much appreciated

I think in your case, the problem is the column name, especially [W/m**2/um/sr].
Also when using PCA, do not forget to rescale the input variables into "comparable" units using StandardScaler.
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
data = pd.DataFrame({'wavelength [um]': [196.078431372549, 1.108370393265022E-003, 192.307692307692], 'intensity [W/m**2/um/sr]': [1.163428008597600E-003, 188.679245283019, 1.223639983609668E-003]})
scaler = StandardScaler(with_mean=True, with_std=True)
pca= PCA(n_components=2)
pca.fit(scaler.fit_transform(data))
print(pca.components_)
Worked well for me. Maybe you just need to specify:
data.columns = data.columns.astype(str)

Normalizing all numeric columns in my dataset and compare before and after

I want to normalize all the numeric values in my dataset.
I have taken my whole dataset into a pandas dataframe.
My code to do this so far:
for column in numeric: #numeric=df._get_numeric_data()
x_array=np.array(df[column])
normalized_X=preprocessing.normalize([x_array])
But how do i verify this is correct though?
I tried plotting a histogram for one of the columns before normalizing and after adding this piece of code before and after my for loop:
x=df['Below.Primary'] #Below.Primary is one of my column names
plt.hist(x, bins=45)
The blue histogram was before the for loop and the orange, after.
My total code looked like this:
ln[21] plt.hist(df['Below.Primary'], bins=45)
ln[22] for column in numeric:
x_array=np.array(df[column])
normalized_X=preprocessing.normalize([x_array])
x=df['Below.Primary']
plt.hist(x, bins=45)
I don't see any reduction in scale. What have i done wrong? If not correct, can someone point out the correct way to do what i wanted to do?

Try use this:
scaler = preprocessing.StandardScaler()
df[col] = scaler.fit_transform(df[col])

A couple general things first.
If numeric is a list of column names (looks like this is the case), the for loop is not necessary.
A Pandas series using an ndarray under the hood so you can just request the ndarray with Series.values instead of calling np.array(). See this page on the Pandas Series.
I am assuming you are using preprocessing from sklearn.
I recommend using sklearn.preprocessing.Normalizer for this.
import pandas as pd
from sklearn.preprocessing import Normalizer
### Without the for loop (recommended)
# this version returns array
normalizer = Normalizer()
normalized_values = normalizer.fit_transform(df[numeric])
# normalized_values is a 2D array which is useful
# for many applications
# to convert back to DataFrame
df = pd.DataFrame(normalized_values, columns = numeric)
### with the for-loop (not recommended)
for column in numeric:
x_array = df[column].values.reshape(-1,1)
df[column] = normalizer.fit_transform(x_array)

You have to set normalized_X to the respective column while iterating.
for column in numeric:
x_array=np.array(df[column])
normalized_X=preprocessing.normalize([x_array])
df[column]= normalized_X #Setting normalized value in the column
x=df['Below.Primary']
plt.hist(x, bins=45)