# How to use log scale with pandas plots

## Question:

I’m making a fairly simple histogram with pandas using

```
results.val1.hist(bins=120)
```

which works fine, but I really want to have a log scale on the y axis, which I normally (probably incorrectly) do like this:

```
fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111)
plt.plot(np.random.rand(100))
ax.set_yscale('log')
plt.show()
```

If I replace the `plt`

command with the pandas command, so I have:

```
fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111)
results.val1.hist(bins=120)
ax.set_yscale('log')
plt.show()
```

results in many copies of the same error:

```
Jan 9 15:53:07 BLARG.local python[6917] <Error>: CGContextClosePath: no current point.
```

I do get a log scale histogram, but it only has the top lines of the bars, but no vertical bars or colors. Am doing something horribly wrong or is this just not supported by pandas?

From Paul H’s code I added `bottom=0.1`

to `hist`

call fixes the problem, I guess there is some kind of divide by zero thing, or something.

## Answers:

Hard to diagnose without any data. The following works for me:

```
import numpy as np
import matplotlib.pyplot as plt
import pandas
series = pandas.Series(np.random.normal(size=2000))
fig, ax = plt.subplots()
series.hist(ax=ax, bins=100, bottom=0.1)
ax.set_yscale('log')
```

The key here is that you pass `ax`

to the histogram function and you specify the `bottom`

since there is no zero value on a log scale.

I’d recommend using the `log=True`

parameter in the pyplot hist function:

Setup step

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame({'column_name': np.random.normal(size=2000)})
```

Using `pyplot`

:

```
plt.hist(df['column_name'], log=True)
```

Or equivalently, you could use the `plot`

method of the dataframe column (series) directly:

```
df["column_name"].plot(kind="hist", logy=True)
```

There’s also `logx`

for log scaling the x-axis and `loglog=True`

for log scaling both axes.

Jean PA’s solution is the simplest, most correct one for this question. Writing this as an answer since I don’t have the rep to comment.

For constructing a histogram straight from pandas, some of the args are passed on to the matplotlib.hist method anyway, so:

```
results.val1.hist(bins = 120, log = True)
```

Would produce what you need.