How to plot aggregated by date pandas dataframe?

When working with time-series data in pandas, you often need to aggregate data by date and visualize the results. This involves grouping data by date periods and plotting the aggregated values using matplotlib.

Basic Date Aggregation and Plotting

Here's how to create and plot a date-aggregated DataFrame ?

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Set figure size
plt.figure(figsize=(10, 6))

# Create sample data with dates and values
dates = pd.date_range("2021-01-01", periods=10, freq='D')
values = np.random.randint(10, 100, 10)

df = pd.DataFrame({'date': dates, 'value': values})
print("Original DataFrame:")
print(df.head())

Original DataFrame:
        date  value
0 2021-01-01     64
1 2021-01-02     52
2 2021-01-03     93
3 2021-01-04     43
4 2021-01-05     29

Aggregating Data by Date

Group the data by date and apply aggregation functions ?

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Create sample data with multiple entries per date
dates = pd.date_range("2021-01-01", periods=20, freq='12H')  # Every 12 hours
values = np.random.randint(10, 100, 20)

df = pd.DataFrame({'date': dates, 'value': values})

# Aggregate by date (sum values for same date)
daily_agg = df.groupby(df['date'].dt.date)['value'].sum().reset_index()
daily_agg['date'] = pd.to_datetime(daily_agg['date'])

print("Aggregated DataFrame:")
print(daily_agg.head())

Aggregated DataFrame:
        date  value
0 2021-01-01    108
1 2021-01-02    156
2 2021-01-03    127
3 2021-01-04    145
4 2021-01-05    163

Plotting the Aggregated Data

Create different types of plots for the aggregated data ?

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Create and aggregate data
dates = pd.date_range("2021-01-01", periods=20, freq='12H')
values = np.random.randint(10, 100, 20)
df = pd.DataFrame({'date': dates, 'value': values})

daily_agg = df.groupby(df['date'].dt.date)['value'].sum().reset_index()
daily_agg['date'] = pd.to_datetime(daily_agg['date'])

# Create subplots
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))

# Line plot
daily_agg.plot(x='date', y='value', kind='line', ax=ax1, marker='o')
ax1.set_title('Daily Aggregated Values - Line Plot')
ax1.set_ylabel('Value')

# Bar plot
daily_agg.plot(x='date', y='value', kind='bar', ax=ax2, color='skyblue')
ax2.set_title('Daily Aggregated Values - Bar Plot')
ax2.set_ylabel('Value')
ax2.tick_params(axis='x', rotation=45)

plt.tight_layout()
plt.show()

Advanced Aggregation Methods

Use different aggregation functions and group by various time periods ?

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Create sample data spanning multiple months
dates = pd.date_range("2021-01-01", periods=90, freq='D')
values = np.random.randint(50, 200, 90)
df = pd.DataFrame({'date': dates, 'value': values})

# Multiple aggregation methods
monthly_agg = df.groupby(df['date'].dt.to_period('M'))['value'].agg(['sum', 'mean', 'count']).reset_index()
monthly_agg['date'] = monthly_agg['date'].dt.to_timestamp()

print("Monthly Aggregation:")
print(monthly_agg)

# Plot multiple aggregations
fig, axes = plt.subplots(1, 3, figsize=(15, 5))

monthly_agg.plot(x='date', y='sum', kind='bar', ax=axes[0], color='lightcoral')
axes[0].set_title('Monthly Sum')

monthly_agg.plot(x='date', y='mean', kind='bar', ax=axes[1], color='lightblue')
axes[1].set_title('Monthly Average')

monthly_agg.plot(x='date', y='count', kind='bar', ax=axes[2], color='lightgreen')
axes[2].set_title('Monthly Count')

plt.tight_layout()
plt.show()

Monthly Aggregation:
      date   sum        mean  count
0 2021-01-01  3823  123.322581     31
1 2021-02-01  3234  115.500000     28
2 2021-03-01  3756  121.161290     31

Comparison of Aggregation Methods

Conclusion

To plot aggregated pandas data by date, use groupby() with date columns and appropriate aggregation functions like sum() or mean(). Use plot() with different kind parameters like 'line' or 'bar' for various visualization styles.