[FIXED] Loop for plots unique values in a dataframe

Issue

I need your help to plot a graph for each item in a dataframe ("Event" column). I created a loop, although the result brings the number of graphs and the title correctly as desired, the graph lines consider the values ​​of the entire dataframe, not considering the filter in the "for - in"

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame()
df['Date']=['2023-07-01', '2023-07-02','2023-07-03','2023-07-04','2023-07-05','2023-07-01', '2023-07-02','2023-07-03','2023-07-04','2023-07-05']
df['Event']=['abc','def','abc','def','abc','def','abc','def','abc','def']
df['Value']=[43,12,19,45,34,21,21,45,38,14]

eve = df['Event'].unique()
for q in eve:
    plt.figure(figsize=(10,6))
    plt.plot(df['Date'],df['Value'], marker='o', linestyle='-', label='Value')
    plt.xticks(rotation=90)
    plt.title(q, fontsize=18)
    plt.show()
plt.close()

The two first images are the obteined output, and the last two plot are the desired output.

Solution

If you remove the .sort_values(by=['Date']) this produces the plots that you want, but to me, it seems very odd to have random dates on the x-axis. Also despite this method being "clean", it is extremely inefficient and won't work well with larger datasets. Let me know if the performance is an issue so I can send you a more optmized version.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame()
df['Date']=['2023-07-01', '2023-07-02','2023-07-03','2023-07-04','2023-07-05','2023-07-01', '2023-07-02','2023-07-03','2023-07-04','2023-07-05']
df['Event']=['abc','def','abc','def','abc','def','abc','def','abc','def']
df['Value']=[43,12,19,45,34,21,21,45,38,14]

unique_events = df['Event'].unique()
for event in unique_events:
    temp = df[df['Event']==event].sort_values(by=['Date'])
    plt.figure(figsize=(10,6))
    plt.plot(temp['Date'],temp['Value'], marker='o', linestyle='-', label='Value')
    plt.xticks(rotation=90)
    plt.title(event, fontsize=18)
    plt.show()

edit: lookup table based approach:

from collections import defaultdict

events_dict = defaultdict(list)
di = df.to_dict()

for row, event in di['Event'].items():
    events_dict[event].append((di['Date'][row], di['Value'][row]))

for event in events_dict:
    plt.figure(figsize=(10,6))
    x,y = zip(*sorted(events_dict[event]))
    plt.plot(x, y, marker='o', linestyle='-', label='Value')
    plt.xticks(rotation=90)
    plt.title(event, fontsize=18)
    plt.show()

performance difference:

As mentioned before, on small datasets it shouldn't matter, but if you work with hundreds of thousands, or millions of rows, the difference start to add up quickly

Answered By - OM222O