Pandas: How to apply numpy function to every column

You can use df.transform(func, axis=0) to apply a numpy function. This leverages the fact that numpy functions work with pandas Series objects.

Example based on How to create pandas time series DataFrame example dataset:

# Load pre-built time series example dataset
df = pd.read_csv("https://datasets.techoverflow.net/timeseries-example.csv", parse_dates=["Timestamp"])
df.set_index("Timestamp", inplace=True)

# np.square will be called individually for each column
new_df = df.transform(np.square, axis=0)

Output

Original time series:

Squared time series:

Full example code

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

# Load pre-built time series example dataset
df = pd.read_csv("https://datasets.techoverflow.net/timeseries-example.csv", parse_dates=["Timestamp"])
df.set_index("Timestamp", inplace=True)

# np.sqrt will be called individually for each column
new_df = df.transform(np.square, axis=0)

# Plot subsection of original DF for better visibility
df.iloc[:len(df)//2].plot()
plt.gcf().set_size_inches(10,5)
plt.savefig("Normal-Timeseries.svg")

# Plot subsection of transformed DF for better visibility
new_df.iloc[:len(df)//2].plot()
plt.gcf().set_size_inches(10,5)
plt.savefig("Square-Timeseries.svg")