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pythonpandasmatplotlibseabornscatter-plot

How to plot a scatter plot on a single y-tick

enter image description here

I'm just curious to know if there is any python package that can be used to plot a Scatter plot on a single axis. I'm open to new ideas and approaches and wanted to put some feelers out before diving into getting started

Can it be possible to plot the above plot with Python if YES, Which package would be used to plot the above plot? Can anybody shed some light on plotting the above plot with Python? I would be happy to receive any leads on it from you.

Exampleset:

timeData = ['2009/6/12 5:00', '2009/6/12 7:00', '2009/6/12 9:00', '2009/6/12 13:00', '2009/6/12 15:00', '2009/6/12 17:00', '2009/6/12 21:00', '2009/6/13 1:00', '2009/6/13 5:00', '2009/6/13 7:00', '2009/6/13 9:00', '2009/6/13 13:00', '2009/6/13 15:00', '2009/6/13 17:00', '2009/6/13 21:00', '2009/6/14 1:00', '2009/6/14 5:00', '2009/6/14 7:00', '2009/6/14 9:00', '2009/6/14 13:00', '2009/6/14 15:00', '2009/6/14 17:00', '2009/6/14 21:00', '2009/6/15 1:00', '2009/6/15 5:00', '2009/6/15 7:00', '2009/6/15 9:00', '2009/6/15 13:00', '2009/6/15 15:00', '2009/6/15 17:00', '2009/6/15 21:00', '2009/6/16 1:00', '2009/6/16 5:00', '2009/6/16 7:00', '2009/6/16 9:00', '2009/6/16 13:00', '2009/6/16 15:00', '2009/6/16 17:00', '2009/6/16 21:00', '2009/6/17 1:00', '2009/6/17 5:00', '2009/6/17 7:00', '2009/6/17 9:00', '2009/6/17 13:00', '2009/6/17 15:00', '2009/6/17 17:00', '2009/6/17 21:00', '2009/6/18 1:00', '2009/6/18 5:00', '2009/6/18 7:00', '2009/6/18 9:00', '2009/6/18 13:00', '2009/6/18 15:00', 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'2009/9/4 23:00', '2009/9/5 3:00', '2009/9/5 5:00', '2009/9/5 7:00', '2009/9/5 11:00', '2009/9/5 15:00', '2009/9/5 19:00', '2009/9/5 21:00', '2009/9/5 23:00', '2009/9/6 3:00', '2009/9/6 5:00', '2009/9/6 7:00', '2009/9/6 11:00', '2009/9/6 15:00', '2009/9/6 19:00', '2009/9/6 21:00', '2009/9/6 23:00', '2009/9/7 3:00', '2009/9/7 5:00', '2009/9/7 7:00', '2009/9/7 11:00', '2009/9/7 15:00', '2009/9/7 19:00', '2009/9/7 21:00', '2009/9/7 23:00', '2009/9/8 3:00', '2009/9/8 5:00', '2009/9/8 7:00', '2009/9/8 11:00', '2009/9/8 15:00', '2009/9/8 19:00', '2009/9/8 21:00', '2009/9/8 23:00', '2009/9/9 3:00', '2009/9/9 5:00', '2009/9/9 7:00', '2009/9/9 11:00', '2009/9/9 15:00', '2009/9/9 19:00', '2009/9/9 21:00', '2009/9/9 23:00', '2009/9/10 3:00', '2009/9/10 5:00', '2009/9/10 7:00', '2009/9/10 11:00', '2009/9/10 15:00', '2009/9/10 19:00', '2009/9/10 21:00', '2009/9/10 23:00', '2009/9/11 3:00', '2009/9/11 5:00', '2009/9/11 7:00', '2009/9/11 11:00', '2009/9/11 15:00', '2009/9/11 19:00', '2009/9/11 21:00', '2009/9/11 23:00', '2009/9/12 3:00', '2009/9/12 5:00', '2009/9/12 7:00', '2009/9/12 11:00', '2009/9/12 15:00', '2009/9/12 19:00', '2009/9/12 21:00', '2009/9/12 23:00', '2009/9/13 3:00', '2009/9/13 5:00', '2009/9/13 7:00', '2009/9/13 11:00', '2009/9/13 15:00', '2009/9/13 19:00', '2009/9/13 21:00', '2009/9/13 23:00', '2009/9/14 3:00', '2009/9/14 5:00', '2009/9/14 7:00', '2009/9/14 11:00', '2009/9/14 15:00', '2009/9/14 19:00', '2009/9/14 21:00', '2009/9/14 23:00', '2009/9/15 3:00', '2009/9/15 5:00', '2009/9/15 7:00', '2009/9/15 11:00', '2009/9/15 15:00', '2009/9/15 19:00', '2009/9/15 21:00', '2009/9/15 23:00', '2009/9/16 3:00', '2009/9/16 5:00', '2009/9/16 7:00', '2009/9/16 11:00', '2009/9/16 15:00', '2009/9/16 19:00', '2009/9/16 21:00', '2009/9/16 23:00', '2009/9/17 3:00', '2009/9/17 5:00', '2009/9/17 7:00', '2009/9/17 11:00', '2009/9/17 15:00', '2009/9/17 19:00', '2009/9/17 21:00', '2009/9/17 23:00', '2009/9/18 3:00', '2009/9/18 5:00', '2009/9/18 7:00', '2009/9/18 11:00', '2009/9/18 15:00', '2009/9/18 19:00', '2009/9/18 21:00', '2009/9/18 23:00', '2009/9/19 3:00', '2009/9/19 5:00', '2009/9/19 7:00', '2009/9/19 11:00', '2009/9/19 15:00', '2009/9/19 19:00', '2009/9/19 21:00', '2009/9/19 23:00', '2009/9/20 3:00', '2009/9/20 5:00', '2009/9/20 7:00', '2009/9/20 11:00', '2009/9/20 15:00', '2009/9/20 19:00', '2009/9/20 21:00', '2009/9/20 23:00', '2009/9/21 3:00', '2009/9/21 5:00', '2009/9/21 7:00', '2009/9/21 11:00', '2009/9/21 15:00', '2009/9/21 19:00', '2009/9/21 21:00', '2009/9/21 23:00', '2009/9/22 3:00', '2009/9/22 5:00', '2009/9/22 7:00', '2009/9/22 11:00', '2009/9/22 15:00', '2009/9/22 19:00', '2009/9/22 21:00', '2009/9/22 23:00', '2009/9/23 3:00', '2009/9/23 5:00', '2009/9/23 7:00', '2009/9/23 11:00', '2009/9/23 15:00', '2009/9/23 19:00', '2009/9/23 21:00', '2009/9/23 23:00', '2009/9/24 3:00', '2009/9/24 5:00', '2009/9/24 7:00', '2009/9/24 11:00', '2009/9/24 15:00', '2009/9/24 19:00', '2009/9/24 21:00', '2009/9/24 23:00', '2009/9/25 3:00', '2009/9/25 5:00', '2009/9/25 7:00', '2009/9/25 11:00', '2009/9/25 15:00', '2009/9/25 19:00', '2009/9/25 21:00', '2009/9/25 23:00', '2009/9/26 3:00', '2009/9/26 5:00', '2009/9/26 7:00', '2009/9/26 11:00', '2009/9/26 15:00', '2009/9/26 19:00', '2009/9/26 21:00', '2009/9/26 23:00', '2009/9/27 3:00', '2009/9/27 5:00', '2009/9/27 7:00', '2009/9/27 11:00', '2009/9/27 15:00', '2009/9/27 19:00', '2009/9/27 21:00', '2009/9/27 23:00', '2009/9/28 3:00', '2009/9/28 5:00', '2009/9/28 7:00', '2009/9/28 11:00', '2009/9/28 15:00', '2009/9/28 19:00', '2009/9/28 21:00', '2009/9/28 23:00', '2009/9/29 3:00', '2009/9/29 5:00', '2009/9/29 7:00', '2009/9/29 11:00', '2009/9/29 15:00', '2009/9/29 19:00', '2009/9/29 21:00', '2009/9/29 23:00', '2009/9/30 3:00', '2009/9/30 5:00', '2009/9/30 7:00', '2009/9/30 11:00', '2009/9/30 15:00', '2009/9/30 19:00', '2009/9/30 21:00', '2009/9/30 23:00', '2009/10/1 3:00', '2009/10/1 5:00', '2009/10/1 7:00', '2009/10/1 11:00', '2009/10/1 15:00', '2009/10/1 19:00', '2009/10/1 21:00', '2009/10/1 23:00', '2009/10/2 3:00', '2009/10/2 5:00', '2009/10/2 7:00', '2009/10/2 11:00', '2009/10/2 15:00', '2009/10/2 19:00', '2009/10/2 21:00', '2009/10/2 23:00', '2009/10/3 3:00', '2009/10/3 5:00', '2009/10/3 7:00', '2009/10/3 11:00', '2009/10/3 15:00', '2009/10/3 19:00', '2009/10/3 21:00', '2009/10/3 23:00', '2009/10/4 3:00', '2009/10/4 5:00', '2009/10/4 7:00', '2009/10/4 11:00', '2009/10/4 15:00', '2009/10/4 19:00', '2009/10/4 21:00', '2009/10/4 23:00', '2009/10/5 3:00', '2009/10/5 5:00', '2009/10/5 7:00', '2009/10/5 11:00', '2009/10/5 15:00', '2009/10/5 19:00', '2009/10/5 21:00', '2009/10/5 23:00', '2009/10/6 3:00', '2009/10/6 5:00', '2009/10/6 7:00', '2009/10/6 11:00', '2009/10/6 15:00', '2009/10/6 19:00', '2009/10/6 21:00', '2009/10/6 23:00', '2009/10/7 3:00', '2009/10/7 5:00', '2009/10/7 7:00', '2009/10/7 11:00', '2009/10/7 15:00', '2009/10/7 19:00', '2009/10/7 21:00', '2009/10/7 23:00', '2009/10/8 3:00', '2009/10/8 5:00', '2009/10/8 7:00', '2009/10/8 11:00', '2009/10/8 15:00', '2009/10/8 19:00', '2009/10/8 21:00', '2009/10/8 23:00', '2009/10/9 3:00', '2009/10/9 5:00', '2009/10/9 7:00', '2009/10/9 11:00', '2009/10/9 15:00', '2009/10/9 19:00', '2009/10/9 21:00', '2009/10/9 23:00', '2009/10/10 3:00', '2009/10/10 5:00', '2009/10/10 7:00', '2009/10/10 11:00', '2009/10/10 15:00', '2009/10/10 19:00', '2009/10/10 21:00', '2009/10/10 23:00', '2009/10/11 3:00', '2009/10/11 5:00', '2009/10/11 7:00', '2009/10/11 11:00', '2009/10/11 15:00', '2009/10/11 19:00', '2009/10/11 21:00', '2009/10/11 23:00', '2009/10/12 3:00', '2009/10/12 5:00', '2009/10/12 7:00', '2009/10/12 11:00', '2009/10/12 15:00', '2009/10/12 19:00', '2009/10/12 21:00', '2009/10/12 23:00', '2009/10/13 3:00', '2009/10/13 5:00', '2009/10/13 7:00', '2009/10/13 11:00', '2009/10/13 15:00', '2009/10/13 19:00', '2009/10/13 21:00', '2009/10/13 23:00', '2009/10/14 3:00', '2009/10/14 5:00', '2009/10/14 7:00', '2009/10/14 11:00', '2009/10/14 15:00', '2009/10/14 19:00', '2009/10/14 21:00', '2009/10/14 23:00', '2009/10/15 3:00', '2009/10/15 5:00', '2009/10/15 7:00', '2009/10/15 11:00', '2009/10/15 15:00', '2009/10/15 19:00', '2009/10/15 21:00', '2009/10/15 23:00', '2009/10/16 3:00', '2009/10/16 5:00', '2009/10/16 7:00', '2009/10/16 11:00', '2009/10/16 15:00', '2009/10/16 19:00', '2009/10/16 21:00', '2009/10/16 23:00', '2009/10/17 3:00', '2009/10/17 5:00', '2009/10/17 7:00', '2009/10/17 11:00', '2009/10/17 15:00', '2009/10/17 19:00', '2009/10/17 21:00', '2009/10/17 23:00', '2009/10/18 3:00', '2009/10/18 5:00', '2009/10/18 7:00']

import pandas as pd

df = pd.DataFrame({'Timestamp': timeData})

df["Timestamp"] = pd.to_datetime(df["Timestamp"], format="%Y/%m/%d %H:%M")



df['hours'] = df["Timestamp"].dt.strftime('%I%p').str.lower().str.lstrip('0')
df['days']  = df["Timestamp"].dt.day_name()

df = pd.crosstab(df['days'], df['hours']).reindex(index=df['days'].unique(), columns=df['hours'].unique())

df

hours   5am 7am 9am 1pm 3pm 5pm 9pm 1am 11am    7pm 11pm    6am 10am    2pm 4pm 6pm 10pm    12am    3am 4am 8am 12pm    8pm
days                                                                                            
Friday  18  14  6   6   17  6   18  4   12  13  15  0   0   0   0   0   0   1   13  0   0   0   0
Saturday    17  14  5   5   17  5   17  5   12  12  15  1   1   1   0   1   1   0   12  1   0   0   1
Sunday  19  15  6   6   18  6   18  6   12  12  16  0   0   0   0   0   0   0   13  0   0   0   0
Monday  18  14  6   6   18  6   18  6   12  12  16  0   0   0   0   0   0   0   12  0   0   0   0
Tuesday 16  14  5   4   17  5   16  6   13  13  16  1   1   1   1   1   1   0   12  0   0   0   0
Wednesday   16  14  4   4   16  5   16  3   14  14  15  0   0   0   0   0   0   1   13  0   0   0   0
Thursday    16  14  4   5   17  4   17  6   13  13  16  0   0   0   1   1   0   0   12  1   1   1   1
Solution

  • Python, Numpy & Matplotlib

    enter image description here

    After ordering the data, it's as simple as plotting 7 scatter plots, taking care of turning off all the unwanted visual elements (left, top, right spines and y ticks & tick labels).

    Just a word on the use of the default property cycle that is forcibly shared among the axes, so that each one is colored differently.

    import numpy as np
import matplotlib.pyplot as plt

# Some constants that will be useful later (I find writing list of strings
# boring and error prone, I prefer to split a string of space separated items)
AMPM_TO_24 = dict(
    zip('12a 1a 2a 3a 4a 5a 6a 7a 8a 9a 10a 11a 12p 1p 2p 3p 4p 5p 6p 7p 8p 9p 10p 11p'.split(),
        '0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23'.split()))
DAYS_OW = 'Sunday Monday Tuesday Wednesday Thursday Friday Saturday'.split()

# I have slightly edited your data: ① shortened am/mp ② used commas
data = '''\
hour,     5a,7a,9a,1p,3p,5p,9p,1a,11a,7p,11p,6a,10a,2p,4p,6p,10p,12a,3a,4a,8a,12p,8p
Friday,   18,14, 6, 6,17, 6,18, 4, 12,13, 15, 0,  0, 0, 0, 0,  0,  1,13, 0, 0,  0, 0
Saturday, 17,14, 5, 5,17, 5,17, 5, 12,12, 15, 1,  1, 1, 0, 1,  1,  0,12, 1, 0,  0, 1
Sunday,   19,15, 6, 6,18, 6,18, 6, 12,12, 16, 0,  0, 0, 0, 0,  0,  0,13, 0, 0,  0, 0
Monday,   18,14, 6, 6,18, 6,18, 6, 12,12, 16, 0,  0, 0, 0, 0,  0,  0,12, 0, 0,  0, 0
Tuesday,  16,14, 5, 4,17, 5,16, 6, 13,13, 16, 1,  1, 1, 1, 1,  1,  0,12, 0, 0,  0, 0
Wednesday,16,14, 4, 4,16, 5,16, 3, 14,14, 15, 0,  0, 0, 0, 0,  0,  1,13, 0, 0,  0, 0
Thursday, 16,14, 4, 5,17, 4,17, 6, 13,13, 16, 0,  0, 0, 1, 1,  0,  0,12, 1, 1,  1, 1'''.split('\n')

# ① extract hours from 1st line, ② convert to 0÷24 and place in a  Numpy array
_, *hours = (h.strip() for h in data[0].split(','))
hours = np.array([AMPM_TO_24[h] for h in hours], dtype=int)
zero = np.zeros_like(hours)

# ① generator of list of items, ② convert data to dict, its values are Numpy arrays
csv = (line.split(',') for line in data[1:])
data = {dow:np.array(values, dtype=int) for dow, *values in csv}

################### PLOTTING STARTS HERE ################
# sharey=all because I want the same scale for all the circles
fig, axes = plt.subplots(7, figsize=(9, 6),
                         sharey='all', constrained_layout=1)
# prop_cycle is renewed for each Axes, we want a single instance for the figure
fig_prop_cycle = plt.rcParams['axes.prop_cycle']()

for ax, dow in zip(axes, DAYS_OW[::-1]):
#or ax, dow in zip(axes, data):  # alternative order of plots
    ax.scatter(hours, [0 for _ in hours], 4*data[dow]**2, **next(fig_prop_cycle), alpha=0.6)
    for x in 'left top right'.split():ax.spines[x].set_visible(False)
    ax.set_xticks(range(0,24,3)) ; ax.set_xlim((-1, 25))
    ax.set_xticks(range(25), minor=True)
    ax.set_yticks([]) ; ax.set_yticklabels([])
    ax.set_ylabel(dow, rotation=0, ha='right', va='center', fontsize=12, fontweight='bold')
plt.show()

    Re previous versions, I didn't realize that sorting the values and later sorting the hours was a sort of no-op (see edit history).

    Re: the ordering of the days.

    Imo this type of graph tends to be read like a page of text, from top to bottom, contrary to a common plot where the ordinates are ordered from bottom to top, hence my idea, presented in a previous revision of this answer, where the days of week were presented in the order in which they are found in the data, from Friday to Thursday.