How to use pandas DataFrame or Series with seaborn compliantly? - python-3.x

Question

I import this dataset and select an interval with
data_frame = pd.read_csv('household_power_consumption.txt',
sep=';',
parse_dates={'dt' : ['Date', 'Time']},
infer_datetime_format=True,
low_memory=False,
na_values=['nan','?'],
index_col='dt')
df_08_09 = data_frame.truncate(before='2008-01-01', after='2010-01-01')
df_08_09.info()
to get
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1052641 entries, 2008-01-01 00:00:00 to 2010-01-01 00:00:00
Data columns (total 7 columns):
Global_active_power 1052641 non-null float64
Global_reactive_power 1052641 non-null float64
Voltage 1052641 non-null float64
Global_intensity 1052641 non-null float64
Sub_metering_1 1052641 non-null float64
Sub_metering_2 1052641 non-null float64
Sub_metering_3 1052641 non-null float64
dtypes: float64(7)
memory usage: 64.2 MB
I just wanted to know how I can treat the DatetimeIndex dt as a data column as well to make use of lmplot() or regplot() as, for, e.g.:
seaborn.regplot(x="dt", y="Global_active_power", data=df_08_09)
The dt is always making problems, because seaborn is not able to access it for some reason. I tried to access the DatetimeIndex, but i found no way to extract it and make it a data column, due to the fact that I'm not quite used to pandas.
I expect seaborn to find dt in the data, but it doesn't and throws an error accordingly. It's clear for me to see that, but idk how to treat this in an efficent python/pandas/seaborn fashion. So please help me out! :)
...An other question btw... I'm also wondering why df_08_09.Global_active_power.values is returning an (n,) shaped np.array and not (n,1). I'm always forced to do values = np.array([values]).transpose() to recover (n,1)

Answer 1

You can use a workaround and convert the datetime-columns to an integer first and replace the axis-tick-labels of matplotlib with the datetime-values afterwards, e.g.
import pandas as pd
import numpy as np
import seaborn
from datetime import datetime
data_frame = pd.read_csv('household_power_consumption.txt',
sep=';',
parse_dates={'dt' : ['Date', 'Time']},
infer_datetime_format=True,
low_memory=False,
na_values=['nan','?'])
#index_col='dt') No need for this, as we are not working with indexes
df_08_09 = data_frame.truncate(before='2008-01-01', after='2010-01-01')
# Convert to integers
df_08_09['date_ordinal'] = pd.to_datetime(df_08_09['dt']).apply(lambda date: date.timestamp())
# Plotting as integers
ax = seaborn.regplot(data=df_08_09, x="date_ordinal", y="Global_active_power")
# Adjust axis
ax.set_xlim(df_08_09['date_ordinal'].min() - 1, df_08_09['date_ordinal'].max() + 1)
ax.set_ylim(0, df_08_09['Global_active_power'].max() + 1)
# Set x-axis-tick-labels to datetime
new_labels = [datetime.utcfromtimestamp(int(item)) for item in ax.get_xticks()]
ax.set_xticklabels(new_labels, rotation = 45)
Reference: This SO answer by waterproof