creating a bubble chart using pandas dataframe - python-3.x
I know this question has been asked before but their solution didn't help me. I have a dataframe of size (391,28) and I want to create a bubble chart like shown here:
bubble chart
My dataframe looks like:
# A B C
# X 0 10 0
# Y 1 2 1
Here I show only a smaller dataframe to give an idea but like I said my dataframe is 391*28 big.
In order to create a bubble chart I do the following:
dfu = df.unstack().reset_index()
dfu.columns = list("XYS")
dfu["S"] *= 20
figure(figsize=(17, 15))
colors = np.random.rand(40)
plt.xticks(rotation=-15)
plt.scatter(x="X", y="Y", s="S", data=dfu)
plt.margins(.4)
plt.show()
But this produces categories on y-axis that are overlapping.
I tried increasing the size but it didn't help. Overlap stays the same. So How can I solve for this? Insights will be appreciated.
Related
How to zoom a matrix that includes NaN values?
I have a map (2D matrix) of observations. Grid boxes without an observed value is assigned a NaN value.
I would like to use the zoom function in python to upscale the size of the grid boxes from 1°x1° to 10°x10°. When I do that, I want to ignore NaN values. For example, in an extreme scenario, if I have 100 1°x1° grid boxes where only one 1°x1° grid box contains an observation and the other 99 1°x1° grid boxes contain NaN, then I want the zoomed out 10°x10° to only take on the value of the single 1°x1° grid box that contained an observation.
Does anyone have a solution for this problem? Let me know if the question is not clear!
Note that it is only in my dataset that the fill value for no observation is NaN. The fill value could be assigned any value. But I want to be able to zoom the grid box sizes with the zooming ignoring the fill values.
Below is an example code that does not do what I want, since the zoom function assigns a NaN value to the 10°x10° grid box if there is one present in the 100 1°x1° it is built from. But I included it as an attempt to illustrate the problem:
import numpy.ma as ma
import numpy as np
from scipy.ndimage.interpolation import zoom
# Create the fake matrix
A = np.random.uniform(0, 10, 10000)
A[A < 5] = np.nan
A = A.reshape(100,100)
# Print the appearance of A
print('A:')
print(A)
# Zoom out to 10 times larger grid boxes
B = zoom(A,(1/10,1/10),order=1)
# Print the appearance of B
print('B:')
print(B)
Creating subplots through a loop from a dataframe
Case:
I receive a dataframe with (say 50) columns.
I extract the necessary columns from that dataframe using a condition.
So we have a list of selected columns of our dataframe now. (Say this variable is sel_cols)
I need a bar chart for each of these columns value_counts().
And I need to arrange all these bar charts in 3 columns, and varying number of rows based on number of columns selected in sel_cols.
So, if say 8 columns were selected, I want the figure to have 3 columns and 3 rows, with last subplot empty or just 8 subplots in 3x3 matrix if that is possible.
I could generate each chart separately using following code:
for col in sel_cols:
df[col].value_counts().plot(kind='bar)
plt.show()
plt.show() inside the loop so that each chart is shown and not just the last one.
I also tried appending these charts to a list this way:
charts = []
for col in sel_cols:
charts.append(df[col].value_counts().plot(kind='bar))
I could convert this list into an numpy array through reshape() but then it will have to be perfectly divisible into that shape. So 8 chart objects will not be reshaped into 3x3 array.
Then I tried creating the subplots first in this way:
row = len(sel_cols)//3
fig, axes = plt.subplots(nrows=row,ncols=3)
This way I would get the subplots, but I get two problems:
I end up with extra subplots in the 3 columns which will go unplotted (8 columns example).
I do not know how to plot under each subplots through a loop.
I tried this:
for row in axes:
for chart, col in zip(row,sel_cols):
chart = data[col].value_counts().plot(kind='bar')
But this only plots the last subplot with the last column. All other subplots stays blank.
How to do this with minimal lines of code, possibly without any need for human verification of the final subplots placements?
You may use this sample dataframe:
pd.DataFrame({'A':['Y','N','N','Y','Y','N','N','Y','N'],
'B':['E','E','E','E','F','F','F','F','E'],
'C':[1,1,0,0,1,1,0,0,1],
'D':['P','Q','R','S','P','Q','R','P','Q'],
'E':['E','E','E','E','F','F','G','G','G'],
'F':[1,1,0,0,1,1,0,0,1],
'G':['N','N','N','N','Y','N','N','Y','N'],
'H':['G','G','G','E','F','F','G','F','E'],
'I':[1,1,0,0,1,1,0,0,1],
'J':['Y','N','N','Y','Y','N','N','Y','N'],
'K':['E','E','E','E','F','F','F','F','E'],
'L':[1,1,0,0,1,1,0,0,1],
})
Selected columns are: sel_cols = ['A','B','D','E','G','H','J','K']
Total 8 columns.
Expected output is bar charts for value_counts() of each of these columns arranged in subplots in a figure with 3 columns. Rows to be decided based on number of columns selected, here 8 so 3 rows.
Given OP's sample data:
df = pd.DataFrame({'A':['Y','N','N','Y','Y','N','N','Y','N'],'B':['E','E','E','E','F','F','F','F','E'],'C':[1,1,0,0,1,1,0,0,1],'D':['P','Q','R','S','P','Q','R','P','Q'],'E':['E','E','E','E','F','F','G','G','G'],'F':[1,1,0,0,1,1,0,0,1],'G':['N','N','N','N','Y','N','N','Y','N'],'H':['G','G','G','E','F','F','G','F','E'],'I':[1,1,0,0,1,1,0,0,1],'J':['Y','N','N','Y','Y','N','N','Y','N'],'K':['E','E','E','E','F','F','F','F','E'],'L':[1,1,0,0,1,1,0,0,1]})
sel_cols = list('ABDEGHJK')
data = df[sel_cols].apply(pd.value_counts)
We can plot the columns of data in several ways (in order of simplicity):
DataFrame.plot with subplots param
seaborn.catplot
Loop through plt.subplots
1. DataFrame.plot with subplots param
Set subplots=True with the desired layout dimensions. Unused subplots will be auto-disabled:
data.plot.bar(subplots=True, layout=(3, 3), figsize=(8, 6),
sharex=False, sharey=True, legend=False)
plt.tight_layout()
2. seaborn.catplot
melt the data into long-form (i.e., 1 variable per column, 1 observation per row) and pass it to seaborn.catplot:
import seaborn as sns
melted = data.melt(var_name='var', value_name='count', ignore_index=False).reset_index()
sns.catplot(data=melted, kind='bar', x='index', y='count',
col='var', col_wrap=3, sharex=False)
3. Loop through plt.subplots
zip the columns and axes to iterate in pairs. Use the ax param to place each column onto its corresponding subplot.
If the grid size is larger than the number of columns (e.g., 3*3 > 8), disable the leftover axes with set_axis_off:
fig, axes = plt.subplots(3, 3, figsize=(8, 8), constrained_layout=True, sharey=True)
# plot each col onto one ax
for col, ax in zip(data.columns, axes.flat):
data[col].plot.bar(ax=ax, rot=0)
ax.set_title(col)
# disable leftover axes
for ax in axes.flat[data.columns.size:]:
ax.set_axis_off()
Alternative to the answer by tdy, I tried to do it without seaborn using Matplotlib and a for loop. Figured it might be better for some who want specific control over subplots with formatting and other parameters, then this is another way: fig = plt.figure(1,figsize=(16,12)) for i, col in enumerate(sel_cols,1): fig.add_subplot(3,4,i,) data[col].value_counts().plot(kind='bar',ax=plt.gca()) plt.title(col) plt.tight_layout() plt.show(1) plt.subplot activates a subplot, while plt.gca() points to the active subplot.
Graphing three database in one graph Python
How can I plot the graph Getting the data from those 3 sources Using only first letter and last digits of the first column to put it in the X-axis as in the Excel graph above How can I only show first column data by 20 digits difference ? aa010 aa030 aa050 ... etc I have three different data from a source. Each one of them has 2 columns. Some of those 3 sources' first columns named the same but each one of them has different data corresponding to it in the second column. I need to use python to plot those 3 data at one graph. X-axis should be the combination of the first column of three data from the sources. - The data is in format of: aa001 - (up to sometimes aa400); ab001 - (up to sometimes ab400). So, the X-axis should start with a aa001 and end with ab400. Since it would just overfill the x-axis and would make it impossible to look at it in a normal size, I want to just show aa020, aa040 ..... (using the number in the string, only show it after aa0(+20) or ab0(+20)) Y-axis should be just numbers from 0-10000 (may want to change if at least one of the data has max more than 10000. I will add the sample graph I created using excel. My sample data would be (Note: Data is not sorted by any column and I would prefer to sort it as stated above: aa001 ...... ab400): Data1 Name Number aa001 123 aa032 4211 ab400 1241 ab331 33 Data2 Name Number aa002 1213 aa032 41 ab378 4231 ab331 63 aa163 999 Data3 Name Number aa209 9876 ab132 5432 ab378 4124 aa031 754 aa378 44 ab344 1346 aa222 73 aa163 414 ab331 61 I searched up Matplotlib, found a sample example where it plots as I want (with dots for each x-y point) but does not apply to my question. This is the similar code I found: x = range(100) y = range(100,200) fig = plt.figure() ax1 = fig.add_subplot(111) ax1.scatter(x[:4], y[:4], s=10, c='b', marker="s", label='first') ax1.scatter(x[40:],y[40:], s=10, c='r', marker="o", label='second') plt.legend(loc='upper left'); plt.show() Sample graph (instead of aa for X-axis-> bc; ab -> mc) I expect to see a graph as follows, but skipping every 20 in the X-axis. (I want the first graph dotted (symbolled) as the second graph but second graph to use X-axis as the first graph, but with skipping 20 in the name First Graph ->- I want to use X-axis like this but without each data (only by 20 difference) Second graph ->- I want to use symbols instead of lines like in this one Please, let me know if I need to provide any other information or clarify/correct myself. Any help is appreciated!
The answer is as following but the following code has still some errors. The final answer will be posted after receiving complete answer at The answer will be in the following link:
Using sorted file to plot X-axis with corresponding Y-values from the original file
from matplotlib import pyplot as plt
import numpy as np
import csv
csv_file = []
with open('hostnum.csv', 'r') as f:
csvreader = csv.reader(f)
for line in csvreader:
csv_file.append(line)
us_csv_file = []
with open('unsorted.csv', 'r') as f:
csvreader = csv.reader(f)
for line in csvreader:
us_csv_file.append(line)
us_csv_file.sort(key=lambda x: csv_list.index(x[1]))
plt.plot([int(item[1]) for item in csvfile], 'o-')
plt.xticks(np.arange(len(csvfile)), [item[0] for item in csvfile])
plt.show()
How to create a scatter plot where values are across multiple columns?
I have a dataframe in Pandas in which the rows are observations at different times and each column is a size bin where the values represent the number of particles observed for that size bin. So it looks like the following: bin1 bin2 bin3 bin4 bin5 Time1 50 200 30 40 5 Time2 60 60 40 420 700 Time3 34 200 30 67 43 I would like to use plotly/cufflinks to create a scatterplot in which the x axis will be each size bin, and the y axis will be the values in each size bin. There will be three colors, one for each observation. As I'm more experienced in Matlab, I tried indexing the values using iloc (note the example below is just trying to plot one observation): df.iplot(kind="scatter",theme="white",x=df.columns, y=df.iloc[1,:]) But I just get a key error: 0 message. Is it possible to use indexing when choosing x and y values in Pandas?
Rather than indexing, I think you need to better understand how pandas and matplotlib interact each other.
Let's go by steps for your case:
As the pandas.DataFrame.plot documentation says, the plotted series is a column. You have the series in the row, so you need to transpose your dataframe.
To create a scatterplot, you need both x and y coordinates in different columns, but you are missing the x column, so you also need to create a column with the x values in the transposed dataframe.
Apparently pandas does not change color by default with consecutive calls to plot (matplotlib does it), so you need to pick a color map and pass a color argument, otherwise all points will have the same color.
Here a working example:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#Here I copied you data in a data.txt text file and import it in pandas as a csv.
#You may have a different way to get your data.
df = pd.read_csv('data.txt', sep='\s+', engine='python')
#I assume to have a column named 'time' which is set as the index, as you show in your post.
df.set_index('time')
tdf = df.transpose() #transpose the dataframe
#Drop the time column from the trasponsed dataframe. time is not a data to be plotted.
tdf = tdf.drop('time')
#Creating x values, I go for 1 to 5 but they can be different.
tdf['xval'] = np.arange(1, len(tdf)+1)
#Choose a colormap and making a list of colors to be used.
colormap = plt.cm.rainbow
colors = [colormap(i) for i in np.linspace(0, 1, len(tdf))]
#Make an empty plot, the columns will be added to the axes in the loop.
fig, axes = plt.subplots(1, 1)
for i, cl in enumerate([datacol for datacol in tdf.columns if datacol != 'xval']):
tdf.plot(x='xval', y=cl, kind="scatter", ax=axes, color=colors[i])
plt.show()
This plots the following image:
Here a tutorial on picking colors in matplotlib.
Matplotlib not plotting sorted dataset in correct order
I have a similar dataset to the following Group Values ------- ------- Group A 5 Group B 2 Group C 10 I sorted the dataset by doing: dataset2 = data.sort_values(by= 'Values', ascending=False) When I display dataset2 it correctly displays the dataset in the correct descending order. However, when I plot dataset2 with matplotlib it plots it using the original data order; not the sorted one. left = dataset2.index.values height = dataset2['Values'] # heights of bars tick_label = dataset2['Group'] # labels for bars # plotting a bar chart plt.bar(left, height, tick_label = tick_label, width = 0.8) plt.xticks(rotation=90) plt.show()
I as able to solve this issue doing two things: 1) making a copy of the sorted dataset. data_sorted = dataset2.copy() 2) Changing the left to: left = np.arange(len(data_sorted)) for some reason when I was using the index to set up the x axis it was using the original index from the original dataset. I thought that when you sorted the values the index was reset.