Python chart: Minimum value

[jamescooper]

Not sure why this doesn't work?

import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt

today_format = datetime.strptime(datetime.today().strftime('%d/%m/%Y'), "%d/%m/%Y")
filtered_df = values=df['Current price'], aggfunc='min') & (df['Date Captured'] == today_format)]
filtered_df.plot(x="Mon Year", y=["Current price"], kind="bar")
plt.title("Minimum Price")
plt.xlabel("Month Year")
plt.ylabel("Price (£)")
plt.locator_params(axis='x', nbins=12)
plt.show()

 

[Cubist]

In your original code, there's a "Mon Year" column. Is that not relevant?

 

[jamescooper]

The Mon Year column is a calculated column in Power Query....

So it is "Jan 25" for example.

 

[Cubist]

I'd like all dates to be shown in the data set.

Do you want to aggregate min on date (d/m/yy) or month-year (m/yy)?
The former will show individual dates since it's taking day into consideration while the later aggregate the entire month.
So either 2/13/25, 2/14/25,..., 3/1/25,3/2/25
or
2/25, 3/25
?

 

[jamescooper]

So along the bottom should be each date ("Check-in") for next 365 days, minimum value....then up the y-axis should be Current price ("Current price") but filtered by todays date as the date captured ("Date captured").

 

[Cubist]

Still don't see how "Mon Year" is relevant. Try:

import pandas as pd
import matplotlib.pyplot as plt
import datetime

df["Date Captured"] = pd.to_datetime(df["Date Captured"])
df["Check-In"] = pd.to_datetime(df["Check-In"])
today = datetime.datetime.today().date()
df_filtered = df[df["Date Captured"].dt.date == today]
grouped = df_filtered.groupby("Check-In")["Current Price"].min().reset_index()

plt.bar(grouped["Check-In"].astype(str), grouped["Current Price"], color='b')
plt.xlabel("Check-In Date")
plt.ylabel("Minimum Price (£)")
plt.title("Minimum Hotel Prices by Check-In Date")
plt.show()

 

[jamescooper]

Still don't see how "Mon Year" is relevant. Try:

import pandas as pd
import matplotlib.pyplot as plt
import datetime

df["Date Captured"] = pd.to_datetime(df["Date Captured"])
df["Check-In"] = pd.to_datetime(df["Check-In"])
today = datetime.datetime.today().date()
df_filtered = df[df["Date Captured"].dt.date == today]
grouped = df_filtered.groupby("Check-In")["Current Price"].min().reset_index()

plt.bar(grouped["Check-In"].astype(str), grouped["Current Price"], color='b')
plt.xlabel("Check-In Date")
plt.ylabel("Minimum Price (£)")
plt.title("Minimum Hotel Prices by Check-In Date")
plt.show()

Thank you, that works just needed to be small p on the "Current price"

 

[jamescooper]

Last thing,

Just need to make sure the x-axis isn't blurred. Any way I can get it to plot month year,

plt.locator_params(axis='x', nbins=12)

Is what I used for other graphs.

 

[Cubist]

plt.locator_params(axis='x', nbins=12)

Is what I used for other graphs.

Make any adjustment you want. I cannot see your charts so cannot comment.

Any way I can get it to plot month year,

Again, I don't know what you want to do with this. You need to communicate better.

 

[jamescooper]

The chart looks like this, you can see the blurred dates at the bottom because there is 365 days on the x-axis. How could I minimise so it is readable as with this other chart off the same dataset.

 

[Cubist]

Try:

import pandas as pd
import matplotlib.pyplot as plt
import datetime
import matplotlib.dates as mdates

df["Date Captured"] = pd.to_datetime(df["Date Captured"])
df["Check-In"] = pd.to_datetime(df["Check-In"])
today = datetime.datetime.today().date()
df_filtered = df[df["Date Captured"].dt.date == today]
grouped = df_filtered.groupby("Check-In")["Current Price"].min().reset_index()

fig, ax = plt.subplots()
ax.bar(grouped["Check-In"], grouped["Current Price"], color='b')

# Formatting the x-axis
ax.xaxis.set_major_locator(mdates.MonthLocator(bymonthday=1))  # Show label on the first of each month
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %Y"))  # Format as "Jan 2025"

plt.xlabel("Check-In Date")
plt.ylabel("Minimum Price (£)")
plt.title("Minimum Hotel Prices by Check-In Date")
plt.xticks(rotation=90)  # Rotate labels
plt.show()