[Niedoszly_Doomer]

Elo mireczki, wiecie jak w matplotlibie zrobić tak aby na tym wykresie nie było rok-miesiac pod kazdym słupkiem tylko jeden rok z ktorego wychodza slupki dla danego roku?
#programowanie #python

[pamareum]

@Niedoszly_Doomer: wrzuć kod

[Niedoszly_Doomer]

@pamareum:
def drawbarplot():
plt.figure(figsize=(14,6))
xvalues = averageviewsbymonth['rok i miesiac']
yvalues = averageviewsbymonth['value']
plt.title('Daily freeCodeCamp Forum Page Views 5/2016-12/2019')
plt.xlabel('date')


plt.bar(xvalues, yvalues, width=0.5)
plt.xticks(averageviewsbymonth['rok i miesiac'])
plt.xticks(rotation=45)

drawbarplot()

w averageviewsbymonth są kolumny rok, miesiac, rok i miesiac oraz value

[Niedoszly_Doomer]

Taki ma byc efekt końcowy

[sokytsinolop]

@Niedoszly_Doomer: https://python-graph-gallery.com/11-grouped-barplot/

[ch1nczyk]

@Niedoszly_Doomer: Masz tu na szybko z ChataGPT:

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

# Create a sample dataframe similar to the user's description
data = {
    'rok i miesiac': [f'{year}-{month:02d}' for year in range(2016, 2020) for month in range(1, 13)],
    'value': np.random.randint(50000, 150000, 48)
}
averageviewsbymonth = pd.DataFrame(data)

# The modified drawbarplot function with sample data
def drawbarplot(averageviewsbymonth):
    # Assuming 'averageviewsbymonth' is a DataFrame with

[Niedoszly_Doomer]

@ch1nczyk: jak sie przeklei kod to działa natomiast w csv dane z 2016 są dopiero od maja i nie przechodzi to na moich danych

[ch1nczyk]

@Niedoszly_Doomer: Sprobuj ten:

def drawbarplot(averageviewsbymonth):
    plt.figure(figsize=(14,6))

    # Assume 'averageviewsbymonth' is a DataFrame with 'Year' and 'Month' as index levels
    # and 'value' as the column with the data to be plotted.
    # We'll create an array of group positions on the X axis for each year:
    years = averageviewsbymonth.index.levels[0]
    months = averageviewsbymonth.index.levels[1]
    n_months = len(months)
    width = 0.1  # Width of the bars
    ind = np.arange(len(years))  # The x locations for