histogram: setting y-axis label for pandas-CodePudding

I have dataframe:

d = {'group': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'D', 'D', 'D', 'D', 'D'], 
     'value': [0.2, 0.4, 0.6, 0.8, 0.2, 0.4, 0.6, 0.8, 0.2, 0.4, 0.6, 0.8, 1.0],
     'count': [4, 3, 7, 3, 12, 14, 5, 10, 3, 8, 7, 15, 4]}
df = pd.DataFrame(data=d)
df

I want to plot multiple histograms in one figure. That is, a histogram for group A, a histogram for group B and group D in one figure. So, labels is a group column, y-axis is count, x-axis is value.

I do this, but there are incorrect values on the y-axis and it builds several figures.

axarr = df.hist(column='value', by = 'group', bins = 20)

for ax in axarr.flatten():
    ax.set_xlabel("value")
    ax.set_ylabel("count")

CodePudding user response：

Assuming that you are looking for a grouped bar plot (pending the clarification in the comments):

Plot:

Code:

import pandas as pd

d = {'group': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'D', 'D', 'D', 'D', 'D'], 
     'value': [0.2, 0.4, 0.6, 0.8, 0.2, 0.4, 0.6, 0.8, 0.2, 0.4, 0.6, 0.8, 1.0],
     'count': [4, 3, 7, 3, 12, 14, 5, 10, 3, 8, 7, 15, 4]}
df = pd.DataFrame(data=d)

df_pivot = pd.pivot_table(
    df,
    values="count",
    index="value",
    columns="group",
)

ax = df_pivot.plot(kind="bar")
fig = ax.get_figure()                
fig.set_size_inches(7, 6)
ax.set_xlabel("value")
ax.set_ylabel("count")

Brief plot option:

pd.pivot(df, index="value", columns="group").plot(kind="bar", y='count')

Brief explanation:

Pivot table for preparation:

x-axis 'value' as index
different bars 'group' as columns

group    A     B     D
value                 
0.2    4.0  12.0   3.0
0.4    3.0  14.0   8.0
0.6    7.0   5.0   7.0
0.8    3.0  10.0  15.0
1.0    NaN   NaN   4.0

Pandas .plot() can handle that groupded bar plot directly after the df_pivot preparation.
Its default backend is matplotlib, so usual commands apply (like fig.savefig).