python: scatter plot logarithmic scale

In my code, I take the logarithm of two data series and plot them. I would like to change each tick value of the x-axis by raising it to the power of e (anti-log of natural logarithm).

In other words. I want to graph the logarithms of both series but have x-axis in levels.

Here is the code that I'm using.

from pylab import scatter
import pylab
import matplotlib.pyplot as plt
import pandas as pd
from pandas import Series, DataFrame
import numpy as np

file_name = '/Users/joedanger/Desktop/Python/scatter_python.csv'

data = DataFrame(pd.read_csv(file_name))

y = np.log(data['o_value'], dtype='float64')
x = np.log(data['time_diff_day'], dtype='float64')

fig = plt.figure()
plt.scatter(x, y, c='blue', alpha=0.05, edgecolors='none')
fig.suptitle('test title', fontsize=20)
plt.xlabel('time_diff_day', fontsize=18)
plt.ylabel('o_value', fontsize=16)
plt.xticks([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4])

plt.grid(True)
pylab.show()

let matplotlib take the log for you:

fig = plt.figure()
ax = plt.gca()
ax.scatter(data['o_value'] ,data['time_diff_day'] , c='blue', alpha=0.05, edgecolors='none')
ax.set_yscale('log')
ax.set_xscale('log')

If you are using all the same size and color markers, it is faster to use plot

fig = plt.figure()
ax = plt.gca()
ax.plot(data['o_value'] ,data['time_diff_day'], 'o', c='blue', alpha=0.05, markeredgecolor='none')
ax.set_yscale('log')
ax.set_xscale('log')

The accepted answer is a bit out of date. At least pandas 0.25 natively supports log axes:

# logarithmic X
df.plot.scatter(..., logx=True)
# logarithmic Y
df.plot.scatter(..., logy=True)
# both
df.plot.scatter(..., loglog=True)