text_html_output_1.png
import matplotlib.pyplot as plt

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns #what is seaborn

df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual = pd.read_csv('/kaggle/input/test.csv')

df_sample['length'] = np.NaN
for i in range(0, len(df_sample['text'])):
    df_sample['length'][i] = len(df_sample['text'][i])
df_sample.length = df_sample.length.astype(int)

sns.set_style('darkgrid')
f, (ax1, ax2) = plt.subplots(figsize=(12, 6), nrows=1, ncols=2, tight_layout=True)
sns.distplot(df_sample[df_sample['target'] == 1]['length'], bins=30, ax=ax1)
sns.distplot(df_sample[df_sample['target'] == 0]['length'], bins=30, ax=ax2)
ax1.set_title('\n Distribution of length of tweet labelled Disaster\n')
ax2.set_title('\nDistribution of length of tweet labelled No Disaster\n ')
ax1.set_ylabel('Frequency')