Pandas plot hist sharex=False does not behave as expected

The sharex (most likely) just falls through to mpl and sets if the panning / zooming one axes changes the other.

The issue you are having is that the same bins are being used for all of the histograms (which is enforced by https://github.com/pydata/pandas/blob/master/pandas/tools/plotting.py#L2053 if I am understanding the code correctly) because pandas assumes that if you multiple histograms then you are probably plotting columns of similar data so using the same binning makes them comparable.

Assuming you have mpl >= 1.5 and numpy >= 1.11 you should write your self a little helper function like

import matplotlib.pyplot as plt
import matplotlib as mpl 
import pandas as pd
import numpy as np

plt.ion()


def make_hists(df, fig_kwargs=None, hist_kwargs=None,
               style_cycle=None):
    '''

    Parameters
    ----------
    df : pd.DataFrame
        Datasource

    fig_kwargs : dict, optional
        kwargs to pass to `plt.subplots`

        defaults to {'fig_size': (4, 1.5*len(df.columns),
                     'tight_layout': True}

    hist_kwargs : dict, optional
        Extra kwargs to pass to `ax.hist`, defaults
        to `{'bins': 'auto'}

    style_cycle : cycler
        Style cycle to use, defaults to 
        mpl.rcParams['axes.prop_cycle']

    Returns
    -------
    fig : mpl.figure.Figure
        The figure created

    ax_list : list
        The mpl.axes.Axes objects created 

    arts : dict 
        maps column names to the histogram artist
    '''
    if style_cycle is None:
        style_cycle = mpl.rcParams['axes.prop_cycle']

    if fig_kwargs is None:
        fig_kwargs = {}
    if hist_kwargs is None:
        hist_kwargs = {}

    hist_kwargs.setdefault('log', True)
    # this requires nmupy >= 1.11
    hist_kwargs.setdefault('bins', 'auto')
    cols = df.columns

    fig_kwargs.setdefault('figsize', (4, 1.5*len(cols)))
    fig_kwargs.setdefault('tight_layout', True)
    fig, ax_lst = plt.subplots(len(cols), 1, **fig_kwargs)
    arts = {}
    for ax, col, sty in zip(ax_lst, cols, style_cycle()):
        h = ax.hist(col, data=df, **hist_kwargs, **sty)
        ax.legend()

        arts[col] = h

    return fig, list(ax_lst), arts

dist = [1, 2, 5, 7, 50]
col_names = ['weibull $a={}$'.format(alpha) for alpha in dist]
test_df = pd.DataFrame(np.random.weibull(dist,
                                         (10000, len(dist))),
                       columns=col_names)

make_hists(test_df)