CODEWITHSUNDEEP
pythonpandasmatplotlib
Patrick.H
Patrick.H

Reputation: 575

matplotlib multiple plots with pandas

I am currently trying to develop a convenience function, which is supposed to create for each column in a pandas dataframe a basic plot with the values and their amount in the dataset for all columns in the dataframe.

def plot_value_counts(df, leave_out):
  # is supposed to create the subplots grid where I can add the plots
  fig, axs = plt.subplots(int(len(df)/2) + 1,int(len(df)/2) + 1)
  for idx, name in enumerate(list(df)):
    if name == leave_out:
      continue
    else:
      axs[idx] = df[name].value_counts().plot(kind="bar")
  return fig, axs

this snippet runs for ever and never stops. I tried looking at other similar questions on stackoverflow, but couldn't find anything specific for my case.

the usage of the subplots function came from the following question: Is it possible to automatically generate multiple subplots in matplotlib?

below a short sample of the data file, so that everybody can understand the problem: https://gist.github.com/hentschelpatrick/e0a7e1400a4b5c356ec8b0e4952f8cc1#file-train-csv

Upvotes: 0

Views: 526

Answers (2)

spidy
spidy

Reputation: 11

Here is a function that i had written for my project to plot all columns in a pandas dataframe. It will generate a grid of size nx4 and will plot all the columns

def plotAllFeatures(dfData):
    plt.figure(1, figsize=(20,50))
    pos=1
    for feature in dfData.columns:
        plt.subplot(np.ceil(len(dfData.columns)/4),4,pos)
        dfData[feature].plot(title=feature)
        pos=pos+1
    plt.show()

Upvotes: 1

Tarifazo
Tarifazo

Reputation: 4343

You can pass the axis object in the plot method docs. And you should iterate on columns:

fig, axs = plt.subplots(int(len(df)/2) + 1,int(len(df)/2) + 1)
for idx, name in enumerate(df.columns):
    if name == leave_out:
        continue
    else:
        df[name].value_counts().plot(kind="bar", ax=axs[idx])

EDIT: If you have memory issues (doens't seem to run) try first without using subplots and show each plot:

for idx, name in enumerate(df.columns):
    if name == leave_out:
        continue
    else:
        df[name].value_counts().plot(kind="bar")
        plt.show()

Upvotes: 1

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