@gray flicker I had dataframe with Year,month and day columns which store their respective numeric values. total 324 rows are there in it. I tried to group them and plot a calendar heatmap on it using their ranks of no. of occurences.
ranks = rts.groupby(['Year','Month','Day']).size().rank(method='min')
when I printed the ranks series,
ranks[:20]
Year Month Day
2021 2 20 1.000
3 1 323.000
5 1.000
11 1.000
19 1.000
5 23 1.000
24 299.000
30 1.000
6 3 317.000
9 1.000
15 227.000
7 2 1.000
3 227.000
5 320.000
6 1.000
9 1.000
10 1.000
11 1.000
16 1.000
18 1.000
This is how my data looks. how do I plot a calendar heatmap using this?
#python calendar heatma[
small swift
gray flickerBOT
<@1082069574901563453> I had dataframe with Year,month and day columns which sto...
Sounds like an interesting task! To plot a calendar heatmap using Python with a dataframe like yours, you can use libraries like matplotlib, seaborn, pandas and calendar.
First, let's create a new dataframe df_ranked with days as the index and months as the columns where each cell contains the rank for that day. Then, we'll use seaborn's heatmap function to create the calendar heatmap.
Here's a general idea of the steps you'd follow:
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import calendar
# Assuming your input dataframe is named 'rts'
ranks = rts.groupby(['Year','Month','Day']).size().rank(method='min')
# Convert Series to DataFrame and reset index
df_ranked = ranks.reset_index(name="Rank")
df_ranked['Day-Month'] = pd.to_datetime(df_ranked[['Year', 'Month', 'Day']])
# Pivot the dataframe to get ranks of days vs month matrix
df_pivot = df_ranked.pivot_table(values="Rank", index="Day", columns="Month", fill_value=0)
#Creating heatmap
f, ax = plt.subplots(figsize=(9, 7))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(240, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(df_pivot, cmap=cmap, vmax=324, center=0,
square=True, linewidths=.5)
# Add month names
ax.set_yticklabels([calendar.month_name[int(x.get_text())] for x in ax.get_yticklabels()])
plt.title('Calendar Heatmap')
plt.show()
Please modify the code to fit your exact needs and specific dataset.
Remember to install the required libraries if you haven't already by using pip install packageName command in your terminal.
--
Keep in mind, Python's heatmaps can be somewhat visually limited for complex calendar analysis. For more sophisticated visualizations, you may want to look into JavaScript libraries such as D3.js, which are more flexible for customized visualizations. Happy plotting! 😊
small swift
@gray flicker not like this....it should have day of month on x axis, month on yaxis and months should be like 2022-09,2022-10,2022-11,.......2023-09,2023-10 and not just 1 to 12
gray flickerBOT
<@1082069574901563453> not like this....it should have day of month on x axis, m...
I see. Let's adjust the approach, then. We’ll use combined Year-Month as Y-axis, Day as X-axis with the heat map color mapping to the rank.
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import calendar
# Assuming your input dataframe is named 'rts'
ranks = rts.groupby(['Year','Month','Day']).size().rank(method='min')
# Convert Series to DataFrame and reset index
df_ranked = ranks.reset_index(name="Rank")
df_ranked['Date'] = pd.to_datetime(df_ranked[['Year', 'Month', 'Day']])
# Extract Year-Month combination and Day separately
df_ranked['Year-Month'] = df_ranked['Date'].dt.to_period('M')
df_ranked['Day'] = df_ranked['Date'].dt.day
# Pivot the dataframe to get ranks of Year-Month vs Day matrix
df_pivot = df_ranked.pivot_table(values="Rank", index="Year-Month", columns="Day", fill_value=0)
df_pivot.sort_index(ascending=False, inplace=True)
#Creating heatmap
fig, ax = plt.subplots(figsize=(18, 9))
sns.heatmap(df_pivot, cmap='YlGnBu', vmin=0, vmax=324, annot=False, ax=ax)
plt.title('Calendar Heatmap')
plt.ylabel('Year-Month')
plt.xlabel('Day of Month')
plt.show()
In this plot, in the y-axis, 'Year-Month', the higher value is no longer at the top as in traditional heatmaps, but at the bottom, giving a more chronological sense. Each cell of the heatmap corresponds to the day of a month, with color indicating the ranked number of occurrences.
Please adapt steps if needed to correctly fit to your data and preferences. Let me know if any further assistance is needed, and enjoy plotting! 😊
small swift
@gray flicker I also want to see the values on each box of the heatmap
gray flickerBOT
<@1082069574901563453> I also want to see the values on each box of the heatmap
Sure thing! If you want to display the values on the heatmap, you just have to turn on the annot parameter in the sns.heatmap function. Let's amend that in the code:
#Creating heatmap with annotations
fig, ax = plt.subplots(figsize=(18, 9))
sns.heatmap(df_pivot, cmap='YlGnBu', vmin=0, vmax=324, annot=True, ax=ax, fmt='d')
plt.title('Calendar Heatmap')
plt.ylabel('Year-Month')
plt.xlabel('Day of Month')
plt.show()
In this case, annot=True will display the values in each cell (you can adjust the font size with annot_kws={"size": 12} added to sns.heatmap parameters if needed) and fmt='d' formats it as an integer decimal.
Now, your new heatmap will include the exact rank values in each cell. Isn't that beautiful 😊?
If you want to further customize the display, seaborn's heatmap offers parameters such as linewidths for lines between cells, cbar to display or hide the color bar, cbar_kws to customize the colorbar, etc. You can document yourself here: https://seaborn.pydata.org/generated/seaborn.heatmap.html.
Go forth and conquer those visualizations, and let me know if there's more help you need with Python, code, or, well, anything!
small swift
@gray flicker if i have a series [0,0,0,1,1,1,2,2,2] and I compute the rank of it, it should give [1,1,1,2,2,2,3,3,3] instead of [1,1,1,4,4,4,7,7,7]