CODEWITHSUNDEEP
pythonpandasseaborn
vishy dewangan
vishy dewangan

Reputation: 1069

Seaborn heatmap throwing unexpected error

I am trying to execute following code:

import seaborn as sns
import pandas as pd
import numpy as np
year = range(1949,1961)
month = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
passengers = pd.Series(np.random.randint(100, 1000, len(year)*len(month)), name='passengers')
years = pd.Series(year * len(month), name='years')
months = pd.Series(month * len(year), name='months').sort_values().reset_index(drop=True)
df = pd.DataFrame([years, months, passengers]).T
df_flight = df.pivot(index="months", columns="years", values="passengers")
sns.heatmap(df_flight, annot=True, fmt="d", linewidths=.5)

It was throwing unexpected error:

TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

Kindly explain what is error in my code.

Upvotes: 2

Views: 1164

Answers (1)

jezrael
jezrael

Reputation: 862601

You need convert values to int by astype, because strings:

sns.heatmap(df_flight.astype(int), annot=True, fmt="d", linewidths=.5)

Problem is if use DataFrame constructor this way and values at least one column are strings, then it convert all values to strings too:

df = pd.DataFrame([years, months, passengers]).T

print (df.dtypes)
years         object
months        object
passengers    object

Solution is use concat or DataFrame constructor with dicts:

df = pd.concat([years, months, passengers], axis=1)

print (df.dtypes)
years          int64
months        object
passengers     int32
dtype: object

...

sns.heatmap(df_flight, annot=True, fmt="d", linewidths=.5)

Or:

df = pd.DataFrame({'years':years, 'months':months, 'passengers':passengers})

print (df.dtypes)
months        object
passengers     int32
years          int64
dtype: object

...

sns.heatmap(df_flight, annot=True, fmt="d", linewidths=.5)

graph

Upvotes: 2

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