I want to plot two DataFrame in a single plot.Though, I have seen similar post but none seems to work out.
First 5 rows of my dataframe looks like this:
df1
name type start stop strand
0 geneA transcript 2000 7764 +
1 geneA exon 2700 5100 +
2 geneA exon 6000 6800 +
3 geneB transcript 9000 12720 -
4 geneB exon 9900 10100 -
df2
P1 P2 P3 P4
0 0.28 0.14 0.19 0.19
1 0.30 0.16 0.17 0.20
2 0.26 0.13 0.20 0.12
3 0.21 0.13 0.25 0.15
4 0.31 0.03 0.24 0.20
I want the plot to look like this:
I tried doing this:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
ax = df1.plot()
df1.plot(ax=ax)
but, the output was not meaningful.
I will appreciate suggestions/solutions on how to achieve this.
Here is a minimal example:
import matplotlib.pyplot as plt
f, axes = plt.subplots(nrows=len(df2.columns)+1, sharex=True, )
# plots for df2 columns
i = 0
for col in df2.columns:
df2[col].plot(ax=axes[i])
axes[i].set_ylim(0, 1.2)
axes[i].set_ylabel(col)
i+=1
## code to plot annotations
# axes[-1].plot(…)
axes[-1].set_xlabel('Genomic position')
# remove space between plots
plt.subplots_adjust(hspace=0)
Here is the full graph:
f, axes = plt.subplots(nrows=len(df2.columns)+1, sharex=True, )
# plots for df2 columns
i = 0
for col in df2.columns:
df2[col].plot(ax=axes[i], color='#505050')
axes[i].set_ylim(0, 1.3)
axes[i].set_ylabel(col)
i+=1
## code to plot annotations
axes[-1].set_xlabel('Genomic position')
axes[-1].set_ylabel('annotations')
axes[-1].set_ylim(-0.5, 1.5)
axes[-1].set_yticks([0, 1])
axes[-1].set_yticklabels(['−', '+'])
for _, r in df1.iterrows():
marker = '|'
lw=1
if r['type'] == 'exon':
marker=None
lw=8
y = 1 if r['strand'] == '+' else 0
axes[-1].plot((r['start'], r['stop']), (y, y),
marker=marker, lw=lw,
solid_capstyle='butt',
color='#505050')
# remove space between plots
plt.subplots_adjust(hspace=0)
You can use subplots for doing so (since it is difficult to understand how the two df should be plotted I've provided a general example)
Import matplotlib.pyplot as plt
fig,axes = plt.subplots(4,1) #4 rows, one column
for ax in axes:
plt.plot(X1,y1 ax =ax) # loop over each subplot and create a plot
plt.plot(X2,y2, ax = ax)
Related
Given a simple pd.Dataframe df that looks like this:
workflow blocked_14 blocked_7 blocked_5 blocked_2 blocked_1
au_in_service_order_response au_in_service_order_response 12.00 11.76 15.38 25.0 0.0
au_in_cats_sync_billing_period au_in_cats_sync_billing_period 3.33 0.00 0.00 0.0 0.0
au_in_MeterDataNotification au_in_MeterDataNotification 8.70 0.00 0.00 0.0 0.0
I want to create a bar-chart that shows the blocked_* columns as the x-axis.
Since df.plot(x='workflow', kind='bar') obviously puts the workflows on the x-axis, I tried ax = blocked_df.plot(x=['blocked_14','blocked_7',...], kind='bar') but this gives me
ValueError: x must be a label or position
How would I create 5 y-Values and have each bar show the according value of the workflow?
Since pandas interprets the x as the index and y as the values you want to plot, you'll need to transpose your dataframe first.
import matplotlib.pyplot as plt
ax = df.set_index('workflow').T.plot.bar()
plt.show()
But that doesn't look too good does it? Let's ensure all of the labels fit on the Axes and move the legend outside of the plot so it doesn't obscure the data.
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(14, 6), layout='constrained')
ax = df.set_index('workflow').T.plot.bar(legend=False, ax=ax)
ax.legend(loc='upper left', bbox_to_anchor=(1, .8))
plt.show()
I have 4 dataframes in 4 csv. I need to plot timeseries ( Date , mean ) in the same plot.
This is my script :
cc = Series.from_csv('D:/python/means2000_2001.csv' , header=0)
fig = plt.figure()
plt.plot(cc , color='red')
fig.suptitle('test title', fontsize=20)
plt.xlabel('Date', fontsize=15)
plt.ylabel('MEANS ', fontsize=15)
plt.xticks(rotation=90)
The 4 dataframes are like this ( x=Date and y=mean )
Out[307]:
Date
07-28 0.17
08-13 0.18
08-29 0.17
09-14 0.19
09-30 0.19
10-16 0.20
11-01 0.18
11-17 0.22
12-03 0.21
12-19 0.82
01-02 0.59
01-18 0.52
02-03 0.54
02-19 0.53
03-07 0.33
03-23 0.32
04-08 0.31
04-24 0.39
05-10 0.40
05-26 0.40
06-11 0.37
06-27 0.33
07-13 0.29
Name: mean, dtype: float64
when I plot the timeseries i have this graph :
how can i plot all dataframes in the same plot with different colors?
I need something like this :
You can do both:
plot all curves with one singel command, see: plt.plot()
adress each singel curve to plot, see for-loop with plt.fill_between()
if you have 2 DataFrames, say df1 and df2, then use plt.plot() twice:
plt.plot(t,df1); plt.plot(t,df2); plt.show()
import numpy as np
import pandas as pd
import matplotlib.pylab as plt
#--- generate data and DataFrame --
nt = 100
t= np.linspace(0,1,nt)*3*np.pi
y1 = np.sin(t); y2 = np.cos(t); y3 = y1*y2
df = pd.DataFrame({'y1':y1,'y2':y2,'y3':y3 })
#--- graphics ---
plt.style.use('fast')
fig, ax0 = plt.subplots(figsize=(20,4))
plt.plot(t,df, lw=4, alpha=0.6); # plot all curves with 1 command
for j in range(len(df.columns)): # add on: fill_between for each curve
plt.fill_between(t,df.values[:,j],label=df.columns[j],alpha=0.2)
plt.legend(prop={'size':15});plt.grid(axis='y');plt.show()
The answer
You can plot multiple dataframes on a single graph by capturing the Axes object that df.plot returns and then reusing it. Here's an example with two dataframes, df1 and df2:
ax = df1.plot(x='dates', y='vals', label='val 1')
df2.plot(x='dates', y='vals', label='val 2', ax=ax)
plt.show()
Output:
Details
Here's the code I used to generate random example values for df1 and df2:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def random_dates(start, end, n=10):
if isinstance(start, str): start = pd.to_datetime(start)
if isinstance(end, str): end = pd.to_datetime(end)
start_u = start.value//10**9
end_u = end.value//10**9
return pd.to_datetime(np.random.randint(start_u, end_u, n), unit='s')
# generate two random dfs
df1 = pd.DataFrame({'dates': random_dates('2016-01-01', '2016-12-31'), 'vals': np.random.rand(10)})
df2 = pd.DataFrame({'dates': random_dates('2016-01-01', '2016-12-31'), 'vals': np.random.rand(10)})
I am trying to plot some data but somehow the data showed on the x-axis is not the proper format. Instead having 2018-01-03 etc I am receiving 0028-02-23. When loading the data the proper format is already loaded when getting the data from the csv file.
In addition I would like to have the data plotted in diverse subplots means valuegroup in subplot 1, valuegroub B in subplot 2 etc.
The figure looks like
The code like:
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter
csv_loader = pd.read_csv('C:/Users/micha/Desktop/Test.csv', encoding='cp1252', parse_dates=['Date'], sep=';', index_col=0).dropna()
fig, ax = plt.subplots()
csv_loader.groupby('Valuegroup').plot(x='Date', y='Value', ax=ax, legend=False, kind='line')
plt.grid(True)
myFmt = DateFormatter("%Y-%m-%d")
ax.xaxis.set_minor_formatter(myFmt)
plt.show()
The data looks like:
Calcgroup;Valuegroup;id;Date;Value
Group1;A;1;20080103;0.1
Group1;A;1;20080104;0.3
Group1;A;1;20080107;0.5
Group1;A;1;20080108;0.9
Group1;B;1;20080103;0.5
Group1;B;1;20080104;1.3
Group1;B;1;20080107;2.0
Group1;B;1;20080108;0.15
Group1;C;1;20080103;1.9
Group1;C;1;20080104;2.1
Group1;C;1;20080107;2.9
Group1;C;1;20080108;0.45
and after importing I have this dataframe:
csv_loader
Valuegroup id Date Value
Calcgroup
Group1 A 1 2008-01-03 0.10
Group1 A 1 2008-01-04 0.30
Group1 A 1 2008-01-07 0.50
Group1 A 1 2008-01-08 0.90
Group1 B 1 2008-01-03 0.50
Group1 B 1 2008-01-04 1.30
Group1 B 1 2008-01-07 2.00
Group1 B 1 2008-01-08 0.15
Group1 C 1 2008-01-03 1.90
Group1 C 1 2008-01-04 2.10
Group1 C 1 2008-01-07 2.90
Group1 C 1 2008-01-08 0.45
Try out this solution
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter
import matplotlib.dates as mdates
csv_loader = pd.read_csv('C:/Users/micha/Desktop/Test.csv', encoding='cp1252', parse_dates=['Date'], sep=';', index_col=0)
fig, ax = plt.subplots()
csv_loader.groupby('Valuegroup').plot(x='Date', y='Value', ax=ax, legend=False, kind='line')
plt.grid(True)
myFmt = DateFormatter('%Y-%m-%d')
ax.xaxis.set_major_locator(mdates.YearLocator())
ax.xaxis.set_major_formatter(myFmt)
fig.autofmt_xdate()
plt.show()
To be honest, I was not able to find what's going wrong with this date format in your code, but however, when at least testing my approch for plotting in separate subplots, which you also asked for, I saw that the formating problem was gone and the automatic format was already the one you want to have:
fig, axs = plt.subplots(3, sharex=True, sharey=True)
for i, (name, grp) in enumerate(csv_loader.groupby('Valuegroup')):
axs[i].plot(grp.Date, grp.Value)
axs[i].set_title(name)
plt.tight_layout()
see yourself:
I am trying to plot my data in the csv file. Currently my dates are not shown properly in the plot also if i am converting it. How can I change it to show the proper dat format as defined Y-m-d? The second question is that I am currently plotting all the dat in one plot but want to have for every Valuegroup one subplot.
My code looks like the following:
import pandas as pd
import matplotlib.pyplot as plt
csv_loader = pd.read_csv('C:/Test.csv', encoding='cp1252', sep=';', index_col=0).dropna()
csv_loader['Date'] = pd.to_datetime(csv_loader['Date'], format="%Y-%m-%d")
print(csv_loader)
fig, ax = plt.subplots()
csv_loader.groupby('Valuegroup').plot(x='Date', y='Value', ax=ax, legend=False, kind='line')
plt.grid(True)
The csv file looks like the following:
Calcgroup;Valuegroup;id;Date;Value
Group1;A;1;20080103;0.1
Group1;A;1;20080104;0.3
Group1;A;1;20080107;0.5
Group1;A;1;20080108;0.9
Group1;B;1;20080103;0.5
Group1;B;1;20080104;1.3
Group1;B;1;20080107;2.0
Group1;B;1;20080108;0.15
Group1;C;1;20080103;1.9
Group1;C;1;20080104;2.1
Group1;C;1;20080107;2.9
Group1;C;1;20080108;0.45
You can just tell pandas to parse that column as a datetime and it will just work:
In[151]:
import matplotlib.pyplot as plt
t="""Calcgroup;Valuegroup;id;Date;Value
Group1;A;1;20080103;0.1
Group1;A;1;20080104;0.3
Group1;A;1;20080107;0.5
Group1;A;1;20080108;0.9
Group1;B;1;20080103;0.5
Group1;B;1;20080104;1.3
Group1;B;1;20080107;2.0
Group1;B;1;20080108;0.15
Group1;C;1;20080103;1.9
Group1;C;1;20080104;2.1
Group1;C;1;20080107;2.9
Group1;C;1;20080108;0.45"""
df = pd.read_csv(io.StringIO(t), parse_dates=['Date'], sep=';', index_col=0)
df
Out[151]:
Valuegroup id Date Value
Calcgroup
Group1 A 1 2008-01-03 0.10
Group1 A 1 2008-01-04 0.30
Group1 A 1 2008-01-07 0.50
Group1 A 1 2008-01-08 0.90
Group1 B 1 2008-01-03 0.50
Group1 B 1 2008-01-04 1.30
Group1 B 1 2008-01-07 2.00
Group1 B 1 2008-01-08 0.15
Group1 C 1 2008-01-03 1.90
Group1 C 1 2008-01-04 2.10
Group1 C 1 2008-01-07 2.90
Group1 C 1 2008-01-08 0.45
fig, ax = plt.subplots()
df.groupby('Valuegroup').plot(x='Date', y='Value', ax=ax, legend=False, kind='line')
plt.grid(True)
plt.show()
results in:
Besides your format string was incorrect anyway, it should be:
csv_loader['Date'] = pd.to_datetime(csv_loader['Date'], format="%Y%m%d")
however, this won't work as that column will have been loaded as int dtype so you would've needed to convert to string first:
csv_loader['Date'] = pd.to_datetime(csv_loader['Date'].astype(str), format="%Y%m%d")
To format the dates on the x-axis you can use DateFormatter from matplotlib see related: Editing the date formatting of x-axis tick labels in matplotlib
from matplotlib.dates import DateFormatter
fig, ax = plt.subplots()
df.groupby('Valuegroup').plot(x='Date', y='Value', ax=ax, legend=False, kind='line')
plt.grid(True)
myFmt = DateFormatter("%d-%m-%Y")
ax.xaxis.set_minor_formatter(myFmt)
plt.show()
now gives plot:
You're parsing your dates wrong; "%Y-%m-%d" would work for dates like 2017-12-11 (which is Dec 12, 2017). Your dates are of the form "%Y%m%d", without the hyphen.
OK So i have this code in Python that Im importing from a csv file the problem is that there are columns in that csv file that aren't basic numbers. There is one column that is text in the format "INT, EXT" and there is a column that is in o'clock format from "0:00 to 11:59" format. I have a third column as a normal number distance in "00.00" format.
My question is how do I go about plotting distance vs o'clock and then basing whether one is INT or EXT changing the colors of the dots for the scatterplot.
My first problem is having how to make the program read oclock format. and text formats from a csv.
Any ideas or suggestions? Thanks in advance
Here is a sample of the CSV im trying to import
ML INT .10 534.15 0:00
ML EXT .25 654.23 3:00
ML INT .35 743.12 6:30
I want to plot the 4th column as the x axis and the 5th column as the y axis
I also want to color code the scatter plot dots red or blue depending if one is INT or EXT
Here is a sample of the code i have so far
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
style.use('ggplot')
a,b,c,d = np.loadtxt('numbers.csv',
unpack = True,
delimiter = ',')
plt.scatter(a,b)
plt.title('Charts')
plt.ylabel('Y Axis')
plt.xlabel('X Axis')
plt.show()
Reading in from your example csv using pandas:
import pandas as pd
import matplotlib.pyplot as plt
import datetime
data = pd.read_csv('data.csv', sep='\t', header=None)
print data
prints:
0 1 2 3 4
0 ML INT 0.10 534.15 0:00
1 ML EXT 0.25 654.23 3:00
2 ML INT 0.35 743.12 6:30
Then separate the 'INT' from the 'EXT':
ints = data[data[1]=='INT']
exts = data[data[1]=='EXT']
change them to datetime and grab the distances:
int_times = [datetime.datetime.time(datetime.datetime.strptime(t, '%H:%M')) for t in ints[4]]
ext_times = [datetime.datetime.time(datetime.datetime.strptime(t, '%H:%M')) for t in exts[4]]
int_dist = [d for d in ints[3]]
ext_dist = [d for d in exts[3]]
then plot a scatter plot for 'INT' and 'EXT' each:
fig, ax = plt.subplots()
ax.scatter(int_dist, int_times, c='orange', s=150)
ax.scatter(ext_dist, ext_times, c='black', s=150)
plt.legend(['INT', 'EXT'], loc=4)
plt.xlabel('Distance')
plt.show()
EDIT: Adding code to answer a question in the comments regarding how to change the time to 12 hour format (ranging from 0:00 to 11:59) and strip the seconds.
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
data = pd.read_csv('data.csv', header=None)
ints = data[data[1]=='INT']
exts = data[data[1]=='EXT']
INT_index = data[data[1]=='INT'].index
EXT_index = data[data[1]=='EXT'].index
time = [t for t in data[4]]
int_dist = [d for d in ints[3]]
ext_dist = [d for d in exts[3]]
fig, ax = plt.subplots()
ax.scatter(int_dist, INT_index, c='orange', s=150)
ax.scatter(ext_dist, EXT_index, c='black', s=150)
ax.set_yticks(np.arange(len(data[4])))
ax.set_yticklabels(time)
plt.legend(['INT', 'EXT'], loc=4)
plt.xlabel('Distance')
plt.ylabel('Time')
plt.show()
I have worked another answer to this, but will leave the original as I believe it's still good, just not exactly answering your particular question.
I also generated a few more rows of data to make the problem, at least on my end, a bit more meaningful.
What solved this for me was generating a 5th column (in code, not the csv) which is the number of minutes corresponding to a particular o'clock time, i.e. 11:59 maps to 719 min. Using pandas I inserted this new column into the dataframe. I could then place string ticklabels for every hour ('0:00', '1:00', etc.) at every 60 min.
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
data = pd.read_csv('Workbook2.csv', header=None)
print data
Prints my faked data:
0 1 2 3 4
0 ML INT 0.10 534.15 0:00
1 ML EXT 0.25 654.23 3:00
2 ML INT 0.30 743.12 6:30
3 ML EXT 0.35 744.20 4:30
4 ML INT 0.45 811.47 7:00
5 ML EXT 0.55 777.90 5:45
6 ML INT 0.66 854.70 7:54
7 ML EXT 0.74 798.40 6:55
8 ML INT 0.87 947.30 11:59
Now make a function to convert o'clock to minutes:
def convert_to_min(o_clock):
h, m = o_clock.split(':')
return int(h) * 60 + int(m)
# using this function create a list times in minutes for each time in col 4
min_col = [convert_to_min(t) for t in data[4]]
data[5] = min_col # inserts this list as a new column '5'
print data
Our new data:
0 1 2 3 4 5
0 ML INT 0.10 534.15 0:00 0
1 ML EXT 0.25 654.23 3:00 180
2 ML INT 0.30 743.12 6:30 390
3 ML EXT 0.35 744.20 4:30 270
4 ML INT 0.45 811.47 7:00 420
5 ML EXT 0.55 777.90 5:45 345
6 ML INT 0.66 854.70 7:54 474
7 ML EXT 0.74 798.40 6:55 415
8 ML INT 0.87 947.30 11:59 719
Now build the x and y axis data, the ticklabels, and the tick locations:
INTs = data[data[1]=='INT']
EXTs = data[data[1]=='EXT']
int_dist = INTs[3] # x-axis data for INT
ext_dist = EXTs[3]
# plotting time as minutes in range [0 720]
int_time = INTs[5] # y-axis data for INT
ext_time = EXTs[5]
time = ['0:00', '1:00', '2:00', '3:00', '4:00', '5:00',
'6:00', '7:00', '8:00', '9:00', '10:00', '11:00', '12:00']
# this will place the strings above at every 60 min
tick_location = [t*60 for t in range(13)]
Now plot:
fig, ax = plt.subplots()
ax.scatter(int_dist, int_time, c='orange', s=150)
ax.scatter(ext_dist, ext_time, c='black', s=150)
ax.set_yticks(tick_location)
ax.set_yticklabels(time)
plt.legend(['INT', 'EXT'], loc=4)
plt.xlabel('Distance')
plt.ylabel('Time')
plt.title('Seems to work...')
plt.show()