We Keep Coding

Creating a multi-bar plot in MatplotLib

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()

how plot multiples dataframe csv in same plot

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)})

Plotting Date fails on x-axis and extension to subplots

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:

Timeseries plot from CSV data (Timestamp and events): x-label constant

(This question can be read alone, but is a sequel to: Timeseries from CSV data (Timestamp and events))
I would like to visualize CSV data (from 2 files) as shown below, by a timeseries representation, using python's pandas module (see links below).
Sample data of df1:
TIMESTAMP eventid
0 2017-03-20 02:38:24 1
1 2017-03-21 05:59:41 1
2 2017-03-23 12:59:58 1
3 2017-03-24 01:00:07 1
4 2017-03-27 03:00:13 1
The 'eventid' column always contains the value of 1, and I am trying to show the sum of events for each day in the dataset.
The 2nd dataset, df0, has similar structure but contains only zeros:
Sample data of df0:
TIMESTAMP eventid
0 2017-03-21 01:38:24 0
1 2017-03-21 03:59:41 0
2 2017-03-22 11:59:58 0
3 2017-03-24 01:03:07 0
4 2017-03-26 03:50:13 0
The x-axis label only shows the same date, and my question is: How can the different dates be shown? (What causes the same date to be shown multiple times on x labels?)
script so far:
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
df1 = pd.read_csv('timestamp01.csv', parse_dates=True, index_col='TIMESTAMP')
df0 = pd.read_csv('timestamp00.csv', parse_dates=True, index_col='TIMESTAMP')
f, (ax1, ax2) = plt.subplots(1, 2)
ax1.plot(df0.resample('D').size())
ax1.set_xlim([pd.to_datetime('2017-01-27'), pd.to_datetime('2017-04-30')])
ax1.xaxis.set_major_formatter(ticker.FixedFormatter
(df0.index.strftime('%Y-%m-%d')))
plt.setp(ax1.xaxis.get_majorticklabels(), rotation=15)
ax2.plot(df1.resample('D').size())
ax2.set_xlim([pd.to_datetime('2017-03-22'), pd.to_datetime('2017-04-29')])
ax2.xaxis.set_major_formatter(ticker.FixedFormatter(df1.index.strftime
('%Y-%m-%d')))
plt.setp(ax2.xaxis.get_majorticklabels(), rotation=15)
plt.show()
Output: (https://www.dropbox.com/s/z21koflkzglm6c3/figure_1.png?dl=0)
Links I have tried to follow:
http://pandas.pydata.org/pandas-docs/stable/visualization.html
Multiple timeseries plots from Pandas Dataframe
Pandas timeseries plot setting x-axis major and minor ticks and labels
Any help is much appreciated.

Making the example reproducible, we can create the following text file (data/timestamp01.csv):
TIMESTAMP;eventid
2017-03-20 02:38:24;1
2017-03-21 05:59:41;1
2017-03-23 12:59:58;1
2017-03-24 01:00:07;1
2017-03-27 03:00:13;1
(same for data/timestamp00.csv). We can then read them in
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
df1 = pd.read_csv('data/timestamp01.csv', parse_dates=True, index_col='TIMESTAMP', sep=";")
df0 = pd.read_csv('data/timestamp00.csv', parse_dates=True, index_col='TIMESTAMP', sep=";")
Plotting them
f, (ax1, ax2) = plt.subplots(1, 2)
ax1.plot(df0.resample('D').size())
ax2.plot(df1.resample('D').size())
plt.setp(ax1.xaxis.get_majorticklabels(), rotation=30, ha="right")
plt.setp(ax2.xaxis.get_majorticklabels(), rotation=30, ha="right")
plt.show()
results in
which is the desired plot.

Pandas dataframe plotting - issue when switching from two subplots to single plot w/ secondary axis

I have two sets of data I want to plot together on a single figure. I have a set of flow data at 15 minute intervals I want to plot as a line plot, and a set of precipitation data at hourly intervals, which I am resampling to a daily time step and plotting as a bar plot. Here is what the format of the data looks like:
2016-06-01 00:00:00 56.8
2016-06-01 00:15:00 52.1
2016-06-01 00:30:00 44.0
2016-06-01 00:45:00 43.6
2016-06-01 01:00:00 34.3
At first I set this up as two subplots, with precipitation and flow rate on different axis. This works totally fine. Here's my code:
import matplotlib.pyplot as plt
import pandas as pd
from datetime import datetime
filename = 'manhole_B.csv'
plotname = 'SSMH-2A B'
plt.style.use('bmh')
# Read csv with precipitation data, change index to datetime object
pdf = pd.read_csv('precip.csv', delimiter=',', header=None, index_col=0)
pdf.columns = ['Precipitation[in]']
pdf.index.name = ''
pdf.index = pd.to_datetime(pdf.index)
pdf = pdf.resample('D').sum()
print(pdf.head())
# Read csv with flow data, change index to datetime object
qdf = pd.read_csv(filename, delimiter=',', header=None, index_col=0)
qdf.columns = ['Flow rate [gpm]']
qdf.index.name = ''
qdf.index = pd.to_datetime(qdf.index)
# Plot
f, ax = plt.subplots(2)
qdf.plot(ax=ax[1], rot=30)
pdf.plot(ax=ax[0], kind='bar', color='r', rot=30, width=1)
ax[0].get_xaxis().set_ticks([])
ax[1].set_ylabel('Flow Rate [gpm]')
ax[0].set_ylabel('Precipitation [in]')
ax[0].set_title(plotname)
f.set_facecolor('white')
f.tight_layout()
plt.show()
2 Axis Plot
However, I decided I want to show everything on a single axis, so I modified my code to put precipitation on a secondary axis. Now my flow data data has disppeared from the plot, and even when I set the axis ticks to an empty set, I get these 00:15 00:30 and 00:45 tick marks along the x-axis.
Secondary-y axis plots
Any ideas why this might be occuring?
Here is my code for the single axis plot:
f, ax = plt.subplots()
qdf.plot(ax=ax, rot=30)
pdf.plot(ax=ax, kind='bar', color='r', rot=30, secondary_y=True)
ax.get_xaxis().set_ticks([])

Here is an example:
Setup
In [1]: from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
%matplotlib inline
df = pd.DataFrame({'x' : np.arange(10),
'y1' : np.random.rand(10,),
'y2' : np.square(np.arange(10))})
df
Out[1]: x y1 y2
0 0 0.451314 0
1 1 0.321124 1
2 2 0.050852 4
3 3 0.731084 9
4 4 0.689950 16
5 5 0.581768 25
6 6 0.962147 36
7 7 0.743512 49
8 8 0.993304 64
9 9 0.666703 81
Plot
In [2]: fig, ax1 = plt.subplots()
ax1.plot(df['x'], df['y1'], 'b-')
ax1.set_xlabel('Series')
ax1.set_ylabel('Random', color='b')
for tl in ax1.get_yticklabels():
tl.set_color('b')
ax2 = ax1.twinx() # Note twinx, not twiny. I was wrong when I commented on your question.
ax2.plot(df['x'], df['y2'], 'ro')
ax2.set_ylabel('Square', color='r')
for tl in ax2.get_yticklabels():
tl.set_color('r')
Out[2]: