I want to create a heat map similar to the picture below using data from an excel sheet.
enter image description here
I tried to use this code below but swap the np.random.seed for data from the excel but it doesnt work. The excel consists of 2 columns and 4 rows of data.
someone please help. Im new to coding and this is super frustrating.
import matplotlib.pyplot as plt
import numpy as np
methods = [None, 'none', 'sinc']
# Fixing random state for reproducibility
np.random.seed(19680801)
grid = np.random.rand(4, 4)
fig, axs = plt.subplots(nrows=3, ncols=6, figsize=(9, 6),
subplot_kw={'xticks': [], 'yticks': []})
for ax, interp_method in zip(axs.flat, methods):
ax.imshow(grid, interpolation=interp_method, cmap='viridis')
ax.set_title(str(interp_method))
plt.tight_layout()
plt.show()
Related
i just stumpled upon a problem I simply cannot solve. I have a dataset with raw data which I will upload here: https://file.io/oJqkZjAGyqV1
Its an excel file with the data inside.
I then created some code to open it, read it, generate a mean and sem of my data as below.
# Import required packages
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from pylab import cm
df = pd.read_excel("Chlorophyll_data_mod.xlsx")
#----Calculation of meanvalues and sem from raw_data---------
meandf2 = df.set_index(["Group"])
sets = []
for x in ["A","B","AB","xc"]:
meandf3 = meandf2.filter(like=f"Chl_{x}_").reset_index()
sets.append(meandf3)
#---------Grouping DataFrame----------#
means = []
ster = []
for x in range(len(sets)):
meandf = sets[x].groupby(["Group"]).mean()
meandf = meandf.reset_index()
means.append(meandf)
sems = sets[x].groupby("Group").sem()
sems = sems.reset_index()
ster.append(sems)
#----Selecting Dataframe from List-----#
plotdf = means[0]
ploter = ster[0]
plotgroup = plotdf.iloc[:,[0,]]
plotdata = plotdf.iloc[:,[1,]]
grouparray = plotgroup.to_numpy()
dataarray = plotdata.to_numpy()
#-----CreatePlot------#
fig, ax = plt.subplots(nrows=3, ncols=1, sharex="all", figsize=(10,8))
plotdf.plot(ax=ax[0,],x="Group",y="Chl_A_0D", kind="bar", legend=False, color="black")
plt.errorbar(x=plotdf["Group"], y=plotdf["Chl_A_0D"],yerr=ploter["Chl_A_0D"])
plotdf.plot(ax=ax[1,],x="Group",y="Chl_A_10DaT", kind="bar", legend=False, color="blue")
plt.errorbar(x=plotdf["Group"], y=plotdf["Chl_A_10DaT"],yerr=ploter["Chl_A_10DaT"])
plotdf.plot(ax=ax[2,],x="Group",y="Chl_A_7DaR", kind="bar", legend=False, color="magenta")
plt.errorbar(x=plotdf["Group"], y=plotdf["Chl_A_7DaR"],yerr=ploter["Chl_A_7DaR"])
#----Legend of the Plot-----#
fig.legend(loc="lower center", bbox_to_anchor=(0.5,0), fancybox=True, ncol=6)
#----Layout------#
plt.tight_layout(rect=[0, 0.02, 1,1])
plt.show()
And I manage to create a subplot, which shows 3 of my interested data points. However, I struggle with the error bars.
My approach was to calculate the sem and store it into a new dataframe. And then just read it from there for the yerr. However, this doesn't work.
plotdf.plot(ax=ax[2,],x="Group",y="Chl_A_7DaR", kind="bar", legend=False, color="magenta", yerr=ploter["Chl_A_7DaR"])
Results in an array error because of the structure.
And my current approach, as in the main code above only draws the error bars in the last subplot, but not in each individual plot.
Maybe here is someone who could help me understanding this function?
Best regards
I'm trying to save my plotted image to local, I've tried to follow some tutorials from the internet, but still can't, can anyone help me?
import pandas as pd
import matplotlib.pyplot as plt
joined_data = pd.read_csv('/content/drive/MyDrive/Kurs/clean_data/forecast.csv')
# First plot
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,8))
ax1.plot(joined_data["kurs_jual"])
ax1.set_xlabel("Tanggal", fontsize=12)
ax1.set_ylabel("Kurs Jual")
ax1.set_title("Kurs Jual VND")
# second plot
ax2.plot(joined_data["kurs_beli"], color="orange")
ax2.set_xlabel("Tanggal", fontsize=12)
ax2.set_ylabel("Kurs Beli")
ax2.set_title("Kurs Beli VND")
plt.show()
plt.savefig('/content/drive/MyDrive/Kurs/clean_data/forecast_vnd.png')
Use plt.savefig('/content/drive/MyDrive/Kurs/clean_data/forecast_vnd.png') before plt.show() else the saved picture would be blank.
I'm using geopandas (python 3.8.2) to plot variables contained in a geodataframe.
I would like to plot on a single figure, all datasets with their own colormap.
The problem is that the plot shows only the last dataset, which corresponds to 'var_5' with colormap 'Reds'. Even if I set: ax = geodataframe.plot() it does not work.
Any idea ? Many Thanks!
import geopandas as gpd
import matplotlib.pyplot as plt
filename = 'myfile.geojson'
geodataframe = gpd.read_file(filename)
cmaps = ['plasma', 'Greens', 'Blues', 'binary', 'Reds']
variables = ['var_1', 'var_2', 'var_3', 'var_4', 'var_5']
plt.rcParams['figure.figsize'] = (20, 10)
ax = plt.gca()
for i, var in enumerate(variables):
geodataframe.plot(ax=ax, column=var, cmap=cmaps[i])
plt.show()
Edit:
After taking into account the answers, I got this image:
I currently am building a set of scatter plot charts using pandas plot.scatter. In this construction off of two base axes.
My current construction looks akin to
ax1 = pandas.scatter.plot()
ax2 = pandas.scatter.plot(ax=ax1)
for dataframe in list:
output_ax = pandas.scatter.plot(ax2)
output_ax.get_figure().save("outputfile.png")
total_output_ax = total_list.scatter.plot(ax2)
total_output_ax.get_figure().save("total_output.png")
This seems inefficient. For 1...N permutations I want to reuse a base axes that has 50% of the data already plotted. What I am trying to do is:
Add base data to scatter plot
For item x in y: (save data to base scatter and save image)
Add all data to scatter plot and save image
here's one way to do it with plt.scatter.
I plot column 0 on x-axis, and all other columns on y axis, one at a time.
Notice that there is only 1 ax object, and I don't replot all points, I just add points using the same axes with a for loop.
Each time I get a corresponding png image.
import numpy as np
import pandas as pd
np.random.seed(2)
testdf = pd.DataFrame(np.random.rand(20,4))
testdf.head(5) looks like this
0 1 2 3
0 0.435995 0.025926 0.549662 0.435322
1 0.420368 0.330335 0.204649 0.619271
2 0.299655 0.266827 0.621134 0.529142
3 0.134580 0.513578 0.184440 0.785335
4 0.853975 0.494237 0.846561 0.079645
#I put the first axis out of a loop, that can be in the loop as well
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(testdf[0],testdf[1], color='red')
fig.legend()
fig.savefig('fig_1.png')
colors = ['pink', 'green', 'black', 'blue']
for i in range(2,4):
ax.scatter(testdf[0], testdf[i], color=colors[i])
fig.legend()
fig.savefig('full_' + str(i) + '.png')
Then you get these 3 images (fig_1, fig_2, fig_3)
Axes objects cannot be simply copied or transferred. However, it is possible to set artists to visible/invisible in a plot. Given your ambiguous question, it is not fully clear how your data are stored but it seems to be a list of dataframes. In any case, the concept can easily be adapted to different input data.
import matplotlib.pyplot as plt
#test data generation
import pandas as pd
import numpy as np
rng = np.random.default_rng(123456)
df_list = [pd.DataFrame(rng.integers(0, 100, (7, 2))) for _ in range(3)]
#plot all dataframes into an axis object to ensure
#that all plots have the same scaling
fig, ax = plt.subplots()
patch_collections = []
for i, df in enumerate(df_list):
pc = ax.scatter(x=df[0], y=df[1], label=str(i))
pc.set_visible(False)
patch_collections.append(pc)
#store individual plots
for i, pc in enumerate(patch_collections):
pc.set_visible(True)
ax.set_title(f"Dataframe {i}")
fig.savefig(f"outputfile{i}.png")
pc.set_visible(False)
#store summary plot
[pc.set_visible(True) for pc in patch_collections]
ax.set_title("All dataframes")
ax.legend()
fig.savefig(f"outputfile_0_{i}.png")
plt.show()
I want to plot several graphs of an excel data in Python with a simple command. I am currently using the following code:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=4, ncols=4, figsize=(12,12))
data_cols = df.columns[8:16]
for data_col, ax in zip(data_cols, axes.ravel()):
ax.boxplot(df[data_col])
I want to set column titles that I choose for data_cols as subtitles of subplots. Do you have an idea about how can I do that?
Thanks in advance.
Of course you need to iterate over those axes and columns you want to plot to and from. So if you want to plot the last 8 columns of the dataframe you also need to iterate over the last 8 axes.
This would then allow you the use the column name as title, ax.set_title(data_col).
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np; np.random.seed(1)
a = np.random.rand(5, 16)
df = pd.DataFrame(a, columns=list("ABCDEFGHIJKLMNOP"))
fig, axes = plt.subplots(nrows=4, ncols=4, figsize=(6,6), sharex=True, sharey=True)
data_cols = df.columns[8:16]
for data_col, ax in zip(data_cols, axes.ravel()[8:16]):
ax.boxplot(df[data_col])
ax.set_title(data_col)
plt.tight_layout()
plt.show()