Seaborn plots not correct

Question

import seaborn as sns, numpy as np
a = np.random.random((20, 20))
mask = np.zeros_like(a)
mask[np.tril_indices_from(mask)] = True #mask the lower triangle
with snenter code heres.axes_style("white"): #make the plot
    ax = sns.heatmap(a, xticklabels=False, yticklabels=False, mask=mask, square=False,  cmap="YlOrRd")
    plt.show()

I make a Seaborn heatmap from an upper triangle numpy array.

This code using pandas:

import pandas as pd
df = pd.read_csv('datatraining.txt', sep=r',', engine='python', header=None, names = ['id', 'date','Temperature','Humidity','Light','CO2','HumidityRatio','Occupancy'])
df = df.drop([0])
df.index = pd.to_datetime(df.date)
df.drop('date', axis=1, inplace=True)
df = df.apply(pd.to_numeric)
def scale(df):
    return (df - df.mean()) / df.std()
df.Temperature = scale(df.Temperature)
df.Humidity = scale(df.Humidity)
df.Light = scale(df.Light)
df.CO2 = scale(df.CO2)
df.HumidityRatio = scale(df.HumidityRatio)

Answer 1

Plots created using seaborn need to be displayed like ordinary matplotlib plots. This can be done using the

plt.show() function from matplotlib.

Originally I posted the solution to use the already imported matplotlib object from seaborn (sns.plt.show()) however this is considered to be a bad practice. Therefore, simply directly import the matplotlib.pyplot module and show your plots with

import matplotlib.pyplot as plt plt.show() If the IPython notebook is used the inline backend can be invoked to remove the necessity of calling show after each plot. The respective magic is

%matplotlib inline

Details which will affect how you store your data, like: Give as much detail as you can; and I can help you develop a structure.

Size of data, # of rows, columns, types of columns; are you appending rows, or just columns? What will typical operations look like. E.g. do a query on columns to select a bunch of rows and specific columns, then do an operation (in-memory), create new columns, save these. (Giving a toy example could enable us to offer more specific recommendations.) After that processing, then what do you do? Is step 2 ad hoc, or repeatable? Input flat files: how many, rough total size in Gb. How are these organized e.g. by records? Does each one contains different fields, or do they have some records per file with all of the fields in each file? Do you ever select subsets of rows (records) based on criteria (e.g. select the rows with field A > 5)? and then do something, or do you just select fields A, B, C with all of the records (and then do something)? Do you 'work on' all of your columns (in groups), or are there a good proportion that you may only use for reports (e.g. you want to keep the data around, but don't need to pull in that column explicity until final results time)?

Answer 2

I come to this question quite regularly and it always takes me a while to find what I search:

import seaborn as sns
import matplotlib.pyplot as plt

plt.show()  # <--- This is what you are looking for

Please note: In Python 2, you can also use sns.plt.show(), but not in Python 3.

Complete Example

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""Visualize C_0.99 for all languages except the 10 with most characters."""

import seaborn as sns
import matplotlib.pyplot as plt

l = [41, 44, 46, 46, 47, 47, 48, 48, 49, 51, 52, 53, 53, 53, 53, 55, 55, 55,
     55, 56, 56, 56, 56, 56, 56, 57, 57, 57, 57, 57, 57, 57, 57, 58, 58, 58,
     58, 59, 59, 59, 59, 59, 59, 59, 59, 60, 60, 60, 60, 60, 60, 60, 60, 61,
     61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 62, 62, 62, 62, 62, 62, 62, 62,
     62, 63, 63, 63, 63, 63, 63, 63, 63, 63, 64, 64, 64, 64, 64, 64, 64, 65,
     65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 66, 66, 66, 66, 66, 66,
     67, 67, 67, 67, 67, 67, 67, 67, 68, 68, 68, 68, 68, 69, 69, 69, 70, 70,
     70, 70, 71, 71, 71, 71, 71, 72, 72, 72, 72, 73, 73, 73, 73, 73, 73, 73,
     74, 74, 74, 74, 74, 75, 75, 75, 76, 77, 77, 78, 78, 79, 79, 79, 79, 80,
     80, 80, 80, 81, 81, 81, 81, 83, 84, 84, 85, 86, 86, 86, 86, 87, 87, 87,
     87, 87, 88, 90, 90, 90, 90, 90, 90, 91, 91, 91, 91, 91, 91, 91, 91, 92,
     92, 93, 93, 93, 94, 95, 95, 96, 98, 98, 99, 100, 102, 104, 105, 107, 108,
     109, 110, 110, 113, 113, 115, 116, 118, 119, 121]

sns.distplot(l, kde=True, rug=False)

plt.show()

Gives

this result