I have a Pandas df of ~1000 tweet ids and their lifetime in seconds (lifetime is the time distance between the first and last retweet). Below is the head of a subset of my df:

Note1: tweets' lifetime is shown in two columns (columns have different dtypes):

1. column lifetime(timedelta) shows tweets lifetime in timedelta64[ns] format,
2. column lifetime(hours) shows tweets lifetime in hours (float64 type). I created column 2 by extracting hours from lifetime(timedelta) column using: df['lifetime_hours'] = df['lifetime(timedelta)'] / np.timedelta64(1, 'h')

Note2: A tweet can belong to more than one type. For example, tweet id:329664 is only type1, while tweet id: 722624 is type1 and type2.

I'd like to plot the distribution of tweets' lifetime for different types of tweets. I could plot the distribution of tweets' lifetime as follows (for all tweets): Here is the bar chart:

and here is the plot:

Here is how I created the above plots (e.g., the bar plot):

bins = range(0, df['lifetime_hours'].max().astype(int), 3) 
data = pd.cut(df['lifetime_hours'], bins, include_lowest=True)

from matplotlib.pyplot import figure
plt.figure(figsize=(20,4))

data.value_counts().sort_index().plot(kind='bar')

plt.xlabel('Tweets Lifetime(hours)')
plt.ylabel('Number of Tweets Active')
plt.title('Distribution of Tweets lifetime')

My question is: How to draw the tweets' lifetime distribution for both types in one plot?

Can someone please help me with this?

CodePudding user response：

• In order to separate the data by types, there should be a single identifier column.
  • This can be created by multiplying the 0 and 1 column values by the column type names, and then joining the column values into a single string as a new column.
• Tested in python 3.10, pandas 1.4.2, matplotlib 3.5.1, seaborn 0.11.2

Imports and DataFrame

import pandas as pd
import numpy as np
import seaborn as sns

# start data
data = {'tweet_id': [329664, 722624, 866498, 156801, 941440],
        'lifetime(timedelta)': [pd.Timedelta('0 days 05:27:22'), pd.Timedelta('0 days 12:43:43'), pd.Timedelta('2 days 09:00:28'),
                                pd.Timedelta('0 days 03:01:29'), pd.Timedelta('0 days 06:39:58')],
        'type1': [1, 1, 0, 1, 0], 'type2': [0, 1, 1, 0, 1], 'type3': [0, 0, 1, 0, 1], 'type4': [0, 0, 0, 0, 1]}
df = pd.DataFrame(data)

# insert hours columns
df.insert(loc=2, column='lifetime(hours)', value=df['lifetime(timedelta)'].div(pd.Timedelta('1 hour')))

# there can be 15 combinations of types for the 4 type columns
# it's best to rename the columns for ease of use
# rename the type columns; can also use df.rename(...)
cols = ['T1', 'T2', 'T3', 'T4']
df.columns = df.columns[:3].tolist()   cols

# create a new column as a unique identifier for types
types = df[cols].mul(cols).replace('', np.nan).dropna(how='all')
df['Types'] = types.apply(lambda row: ' '.join(row.dropna()), axis=1)

# create a column for the bins
bins = range(0, df['lifetime(hours)'].astype(int).add(4).max(), 3) 
df['Tweets Liftime(hours)'] = pd.cut(df['lifetime(hours)'], bins, include_lowest=True)

# display(df)
   tweet_id lifetime(timedelta)  lifetime(hours)  T1  T2  T3  T4     Types Tweets Liftime(hours)
0    329664     0 days 05:27:22         5.456111   1   0   0   0        T1            (3.0, 6.0]
1    722624     0 days 12:43:43        12.728611   1   1   0   0     T1 T2          (12.0, 15.0]
2    866498     2 days 09:00:28        57.007778   0   1   1   0     T2 T3          (57.0, 60.0]
3    156801     0 days 03:01:29         3.024722   1   0   0   0        T1            (3.0, 6.0]
4    941440     0 days 06:39:58         6.666111   0   1   1   1  T2 T3 T4            (6.0, 9.0]

Create a Frequency Table

ct = pd.crosstab(df['Tweets Liftime(hours)'], df['Types'])

# display(ct)
Types                  T1  T1 T2  T2 T3  T2 T3 T4
Tweets Liftime(hours)                            
(3.0, 6.0]              2      0      0         0
(6.0, 9.0]              0      0      0         1
(12.0, 15.0]            0      1      0         0
(57.0, 60.0]            0      0      1         0

Plot

pandas.DataFrame.plot

• Uses ct

ax = ct.plot(kind='bar', figsize=(20, 5), width=0.1, rot=0)
ax.set(ylabel='Number of Tweets Active', title='Distribution of Tweets Lifetime')
ax.legend(title='Types', bbox_to_anchor=(1, 1), loc='upper left')

seaborn.catplot

• Uses df without the need to reshape

p = sns.catplot(kind='count', data=df, x='Tweets Liftime(hours)', height=4, aspect=4, hue='Types')
p.set_xticklabels(rotation=45)
p.fig.subplots_adjust(top=0.9)
p.fig.suptitle('Distribution of Tweets Lifetime')
p.axes[0, 0].set_ylabel('Number of Tweets Active')