Python pandas: how to remove nan and -inf values

Use pd.DataFrame.isin and check for rows that have any with pd.DataFrame.any. Finally, use the boolean array to slice the dataframe.

df[~df.isin([np.nan, np.inf, -np.inf]).any(1)]

             time    X    Y  X_t0     X_tp0   X_t1     X_tp1   X_t2     X_tp2
4        0.037389    3   10     3  0.333333    2.0  0.500000    1.0  1.000000
5        0.037393    4   10     4  0.250000    3.0  0.333333    2.0  0.500000
1030308  9.962213  256  268   256  0.000000  256.0  0.003906  255.0  0.003922

You can replace inf and -inf with NaN, and then select non-null rows.

df[df.replace([np.inf, -np.inf], np.nan).notnull().all(axis=1)]  # .astype(np.float64) ?

or

df.replace([np.inf, -np.inf], np.nan).dropna(axis=1)

Check the type of your columns returns to make sure they are all as expected (e.g. np.float32/64) via df.info().

df.replace([np.inf, -np.inf], np.nan)

df.dropna(inplace=True)

Instead of dropping rows which contain any nulls and infinite numbers, it is more succinct to the reverse the logic of that and instead return the rows where all cells are finite numbers. The numpy isfinite function does this and the '.all(1)' will only return a TRUE if all cells in row are finite.

df = df[np.isfinite(df).all(1)]

Edit: If you have some non-numerical dtypes in your dataframe, you might want to isolate the float dtype columns of interest. See example below.

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.rand(3,4), columns=list("ABCD"))
df['E'] = 'a_string'
df.at[2,'D'] = np.nan
df.at[1,'B'] = np.inf
df['A'] = df['A'].astype(np.float16)
df
          A         B         C         D         E
0  0.325195  0.199801  0.175851  0.989883  a_string
1  0.040192       inf  0.296379  0.632059  a_string
2  0.348877  0.369374  0.976187       NaN  a_string

floating_columns = df.select_dtypes(include=[np.floating]).columns
subset_df = df[floating_columns]
df = df[np.isfinite(subset_df).all(1)]
df
          A         B         C         D         E
0  0.381104  0.119991  0.388697  0.235735  a_string