I am trying to plot multiple graphs into 1 using a for loop and I encountered this problem. I tried it for the other loops and it works just fine but I don't know what happened with this one.
The files used are the exchange rates of EUR to USD for the past 2 years and I am trying to plot the date and the price on the graph. If I don't use figsize the graph is too small but it works.
import pandas as pd
import matplotlib.pyplot as plt
file = ['somefile.csv', 'otherfile.csv', 'anotherfile.csv']
for files in file:
files1 = pd.read_csv ('%s' %files)
files1.plot (kind='line', x='Date', y='Price', ax=ax, figsize=(15,10))
plt.legend()
plt.show()
One way around is using
plt.gcf().set_size_inches(15, 8)
So your code should be
import pandas as pd
import matplotlib.pyplot as plt
file = ['somefile.csv', 'otherfile.csv', 'anotherfile.csv']
for files in file:
files1 = pd.read_csv ('%s' %files)
files1.plot (kind='line', x='Date', y='Price', ax=ax)
plt.gcf().set_size_inches(15, 8))
plt.legend()
plt.show()
Use the following: Create first the axis object specifying the figure size and then use that object while plotting
fig, ax = plt.subplots(figsize=(15,10))
for files in file:
files1 = pd.read_csv ('%s' %files)
files1.plot (kind='line', x='Date', y='Price', ax=ax)
Since you want to specify axis as ax(ax=ax), best answer would be #Sheldore's. But there's actually an easier, also the most basic, way to this:
plt.figure(figsize=(15,10))
Put this before the for loop.
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 have produced a Boxplot/Swarmplot graph using Matplotlib/Seaborn in Pandas. Some outliers can been seen in the graph (as dots outside the "whiskers"/"fence" area). I am looking for a way to trim the dataset directly after they have been identified in the graph and without removing them from the original dataset. I do not want to simply hide the outlier dots.
Some methods have been recommended and pandas quantile looks promising but I am not sure how to implement these with the code I have been using.
My graph with the outliers.
The code I used to produce this graph. The data has been organized into the tidy format.
# Import libraries and modules
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Set seaborn style
sns.set(style="whitegrid", palette="colorblind")
# load length tidy data
length_tidy = pd.read_csv('results/tidy/length_tidy.csv')
score_tidy = pd.read_csv('results/tidy/score_tidy.csv')
# Define and save boxplot and swarmplot for length data
fig, ax = plt.subplots(figsize=(10,6))
ax = sns.boxplot(x='Metric', y='Length', data=length_tidy, ax=ax)
ax = sns.swarmplot(x="Metric", y="Length", data=length_tidy, color=".25")
ax.set_xlabel('Condition')
ax.set_ylabel('Length in micrometers')
plt.savefig('statistics/boxplot/length_boxplot.png', dpi=300)
fig, ax = plt.subplots(figsize=(10,6))
ax = sns.boxplot(x='Metric', y='Score', data=score_tidy, ax=ax)
ax = sns.swarmplot(x="Metric", y="Score", data=score_tidy, color=".25")
ax.set_xlabel('Condition')
ax.set_ylabel('Score')
plt.savefig('statistics/boxplot/score_boxplot.png', dpi=300)
An example of some of the data I am working with in the CSV format.
Object,Metric,Length
M11,B2A10,1.807782
MT1,B2A10,3.2207116666666664
MT1,B2A1,3.57675
MT1,B2A2,2.9474600000000004
MT1,B2A3,2.247772857142857
MT1,B2A4,3.754455
MT1,B2A5,2.716282
MT1,B2A6,2.91325
MT1,B2A7,1.24806
MT1,B2A8,2.00371875
MT1,B2A9,1.5435599999999998
MT1,B2B1,2.2051515384615388
MT1,B2B2,1.5278873333333332
MT1,B2B3,1.7283750000000002
MT1,B2B4,1.4547385714285714
MT1,B2B5,3.237578333333333
MT1,B2B6,2.47016
MT1,B2B7,2.1185947777777776
MT1,B2B8,1.8502877777777773
MT10,B2A10,3.07143
MT10,B2A1,3.34361
MT10,B2A2,2.889958333333333
MT10,B2A3,2.22087
MT10,B2A4,2.87669
MT10,B2A5,1.6745005555555557
MT10,B2A7,2.09018
MT10,B2A8,2.4947450000000004
MT10,B2B1,1.849095882352941
MT10,B2B2,1.5291758000000002
MT10,B2B5,1.6423770999999998
MT10,B2B6,1.9680385714285715
MT10,B2B7,1.7207240000000001
MT10,B2B8,2.9618275
MT12,B2A10,1.7243058333333334
MT12,B2A1,3.3938900000000003
MT12,B2A2,2.00601
MT12,B2A3,2.1720200000000003
MT12,B2A4,2.452923333333333
MT12,B2A5,2.986948
MT12,B2A7,2.08466
MT12,B2A8,1.29047
MT12,B2B1,2.528839230769232
MT12,B2B2,1.4011425454545454
MT12,B2B5,1.626078333333333
MT12,B2B6,1.074394454545455
MT12,B2B7,2.0897078571428573
MT12,B2B8,1.4102533333333336
so I am plotting error bar of pandas dataframe. Now the error bar has a weird arrow at the top, but what I want is a horizontal line. For example, a figure like this:
But now my error bar ends with arrow instead of a horinzontal line.
Here is the code i used to generate it:
plot = meansum.plot(
kind="bar",
yerr=stdsum,
colormap="OrRd_r",
edgecolor="black",
grid=False,
figsize=(8, 2),
ax=ax,
position=0.45,
error_kw=dict(ecolor="black", elinewidth=0.5, lolims=True, marker="o"),
width=0.8,
)
So what should I change to make the error become the one I want. Thx.
Using plt.errorbar from matplotlib makes it easier as it returns several objects including the caplines which contain the marker you want to change (the arrow which is automatically used when lolims is set to True, see docs).
Using pandas, you just need to dig the correct line in the children of plot and change its marker:
import pandas as pd
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
df = pd.DataFrame({"val":[1,2,3,4],"error":[.4,.3,.6,.9]})
meansum = df["val"]
stdsum = df["error"]
plot = meansum.plot(kind='bar',yerr=stdsum,colormap='OrRd_r',edgecolor='black',grid=False,figsize=8,2),ax=ax,position=0.45,error_kw=dict(ecolor='black',elinewidth=0.5, lolims=True),width=0.8)
for ch in plot.get_children():
if str(ch).startswith('Line2D'): # this is silly, but it appears that the first Line in the children are the caplines...
ch.set_marker('_')
ch.set_markersize(10) # to change its size
break
plt.show()
The result looks like:
Just don't set lolim = True and you are good to go, an example with sample data:
import pandas as pd
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
df = pd.DataFrame({"val":[1,2,3,4],"error":[.4,.3,.6,.9]})
meansum = df["val"]
stdsum = df["error"]
plot = meansum.plot(kind='bar',yerr=stdsum,colormap='OrRd_r',edgecolor='black',grid=False,figsize=(8,2),ax=ax,position=0.45,error_kw=dict(ecolor='black',elinewidth=0.5),width=0.8)
plt.show()
I try to plot multi-line with different attribute(color, line-type, etc) with pandas grouby data set. My code plots all blue line of multiple source.
How to apply line attribute at each group?
My code is bleow.
from pandas import Series, DataFrame
import pandas as pd
import matplotlib.pyplot as plt
xls_file = pd.ExcelFile(r'E:\SAT_DATA.xlsx')
glider_data = xls_file.parse('Yosup (4)', parse_dates=[0])
each_glider = glider_data.groupby('Vehicle')
fig, ax = plt.subplots(1,1);
glider_data.groupby("Vehicle").plot(x="TimeStamp", y="Temperature(degC)", ax=ax)
plt.legend(glider_data['Vehicle'], loc='best')
plt.xlabel("Time")
plt.ylabel("Temp")
plt.show()
I think you need to loop over the groups from groupby. Something like:
for i,group in glider_data.groupby('Vehicle'):
group.plot(x='TimeStamp', y='Temperature(degC)', ax=ax, label=i)