Pandas dataframe columns sorted while plotting

Question

I have a pandas dataframe with multiple columns like following:

columns_all = pd.DataFrame({'m1_h':m1_hist, 'm2_h':m2_hist, ....... 'm6_h':m6_hist, 'm6_f':m6_futu})

and I use following to plot histograms based on each column but columns are sorted But I like to have all histograms in same order as columns written in above dataframe:

columns_all.hist(layout=(2,6), sharey=True, sharex=True)
plt.ylim(0, 100)
plt.xlim(0, 150)
plt.show()

Appreciate any suggestion to maintain order of columns while plotting.

Y. Luo · Accepted Answer · 2017-06-15 04:57:42Z

According to the source code, the sorting is defined by _try_sort(data.columns) and cannot be changed by an argument. You can do what Claudiu Creanga suggested. However, in my test, that won't give you a (2, 6) layout. If you really want that layout and what pandas.DataFrame.hist does, the following code may be helpful:

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

columns_all = pd.DataFrame([np.random.randn(1000)] * 7).T
columns_all.columns = ['m1_h', 'm2_h', 'm3_h', 'm4_h', 'm5_h', 'm6_h', 'm6_f']
plt.clf()
fig = plt.figure(figsize=(16, 4))
axarr = []
for i, col in enumerate(columns_all.columns):
    if i // 6 > 0:
        sharex = axarr[i % 6]
        plt.setp(axarr[i % 6].get_xticklabels(), visible=False)
    else:
        sharex = None
    if i % 6 > 0:
        sharey = axarr[i // 6]
    else:
        sharey = None
    ax = fig.add_subplot(2, 6, i + 1, sharex=sharex, sharey=sharey)
    axarr.append(ax)
    if i % 6 > 0:
        plt.setp(ax.get_yticklabels(), visible=False)
    ax.hist(columns_all[col].dropna().values)
    ax.set_title(col)
    ax.grid(True)
fig.subplots_adjust(wspace=0.3, hspace=0.3)
plt.show()

Claudiu Creanga · Accepted Answer · 2017-06-15 04:12:54Z

1

You could make repeated calls to individual columns, as both on the creation of the dataframe and on the .hist() there is a reordering done automatically:

s = pd.DataFrame([{'B': 1.5, 'A':3, 'C': 4, 'D':2}])
s

    A   B   C   D
0   3   1.5 4   2

s = s[["B", "A", "C", "D"]] #chose your order
s

    B   A   C   D
0   1.5 3   4   2

for x in s.columns:
    s[[x]].hist(layout=(2,6), sharey=True, sharex=True)
plt.ylim(0, 100)
plt.xlim(0, 150)
plt.show()

answered Jun 15, 2017 at 4:12

Claudiu Creanga

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