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Data Limits and Maximum Distances for boxplot in pandas (Python)

1

I am using Python to plot data (coming from many experiments) and I would like to use boxplot method of pandas library.

Executing df = pd.DataFrame(value,columns=['Col1']) the result is the following one:

The problem comes from the extreme values. In Matlab the solution is to use the 'DataLimit' option:

boxplot(bp1,'DataLim',[4.2,4.3])

From Matlab documentation:

Data Limits and Maximum Distances

'DataLim' — Extreme data limits
[-Inf,Inf] (default) | two-element numeric vector

Extreme data limits, specified as the comma-separated pair consisting of 'DataLim' and a two-element numeric vector containing the lower and upper limits, respectively. The values specified for 'DataLim' are used by 'ExtremeMode' to determine which data points are extreme.

Is there something similar for Python?

Walkaround:
However, I have a walk around (that I really don't like because it changes the statistical distribution of the measurements): I just exclude the "problematic values" manually:

df = pd.DataFrame(value[100:],columns=['Col1'])
df.boxplot(column=['Col1'])

and the result is:

This is because I know where the problem is.

python python-3.x pandas matlab boxplot

share|improve this question

edited 2 days ago

Leos313

asked 2 days ago

Leos313Leos313

1,61711435

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Couldn't you just filter your df with loc before plotting?
  
  – Josh Friedlander
  2 days ago
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  I don't think there is an option in matplotlib to do exactly what you want. I would just plot the filtered df df[(df["Col1"] > 4.2) & (df["Col1"] < 4.3)].boxplot()
  
  – Runkles
  2 days ago
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Josh, what do you mean? can you make an example?
  
  – Leos313
  2 days ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Runkles yes, it can work. But I think (not sure!!) that in Matlab the points are used for the statistics of the boxplot and just not printed
  
  – Leos313
  2 days ago
  
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Runkles if you plot only those data, you change the statistical distribution. Not sure if that's ok for OP
  
  – micric
  2 days ago
  

  
  
  
  

 | 
show 2 more comments

1

I am using Python to plot data (coming from many experiments) and I would like to use boxplot method of pandas library.

Executing df = pd.DataFrame(value,columns=['Col1']) the result is the following one:

The problem comes from the extreme values. In Matlab the solution is to use the 'DataLimit' option:

boxplot(bp1,'DataLim',[4.2,4.3])

From Matlab documentation:

Data Limits and Maximum Distances

'DataLim' — Extreme data limits
[-Inf,Inf] (default) | two-element numeric vector

Extreme data limits, specified as the comma-separated pair consisting of 'DataLim' and a two-element numeric vector containing the lower and upper limits, respectively. The values specified for 'DataLim' are used by 'ExtremeMode' to determine which data points are extreme.

Is there something similar for Python?

Walkaround:
However, I have a walk around (that I really don't like because it changes the statistical distribution of the measurements): I just exclude the "problematic values" manually:

df = pd.DataFrame(value[100:],columns=['Col1'])
df.boxplot(column=['Col1'])

and the result is:

This is because I know where the problem is.

python python-3.x pandas matlab boxplot

share|improve this question

edited 2 days ago

Leos313

asked 2 days ago

Leos313Leos313

1,61711435

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Couldn't you just filter your df with loc before plotting?
  
  – Josh Friedlander
  2 days ago
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  I don't think there is an option in matplotlib to do exactly what you want. I would just plot the filtered df df[(df["Col1"] > 4.2) & (df["Col1"] < 4.3)].boxplot()
  
  – Runkles
  2 days ago
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Josh, what do you mean? can you make an example?
  
  – Leos313
  2 days ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Runkles yes, it can work. But I think (not sure!!) that in Matlab the points are used for the statistics of the boxplot and just not printed
  
  – Leos313
  2 days ago
  
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Runkles if you plot only those data, you change the statistical distribution. Not sure if that's ok for OP
  
  – micric
  2 days ago
  

  
  
  
  

 | 
show 2 more comments

I am using Python to plot data (coming from many experiments) and I would like to use boxplot method of pandas library.

Executing df = pd.DataFrame(value,columns=['Col1']) the result is the following one:

The problem comes from the extreme values. In Matlab the solution is to use the 'DataLimit' option:

boxplot(bp1,'DataLim',[4.2,4.3])

From Matlab documentation:

Data Limits and Maximum Distances

'DataLim' — Extreme data limits
[-Inf,Inf] (default) | two-element numeric vector

Extreme data limits, specified as the comma-separated pair consisting of 'DataLim' and a two-element numeric vector containing the lower and upper limits, respectively. The values specified for 'DataLim' are used by 'ExtremeMode' to determine which data points are extreme.

Is there something similar for Python?

Walkaround:
However, I have a walk around (that I really don't like because it changes the statistical distribution of the measurements): I just exclude the "problematic values" manually:

df = pd.DataFrame(value[100:],columns=['Col1'])
df.boxplot(column=['Col1'])

and the result is:

This is because I know where the problem is.

python python-3.x pandas matlab boxplot

share|improve this question

edited 2 days ago

Leos313

asked 2 days ago

Leos313Leos313

1,61711435

boxplot(bp1,'DataLim',[4.2,4.3])

df = pd.DataFrame(value[100:],columns=['Col1'])
df.boxplot(column=['Col1'])

share|improve this question

edited 2 days ago

Leos313

asked 2 days ago

Leos313Leos313

1,61711435

share|improve this question

edited 2 days ago

Leos313

asked 2 days ago

Leos313Leos313

1,61711435

asked 2 days ago

Leos313Leos313

1,61711435

asked 2 days ago

Leos313Leos313

1,61711435

1 Answer
1

active

oldest

votes

0

You can use ylim to constrain the axis without omitting the outliers from the calculation:

data = np.concatenate((np.random.rand(50) * 100, # spread
 np.ones(25) * 50, # center
 np.random.rand(10) * 100 + 100, # flier high
 np.random.rand(10) * -100, # flier low
 np.random.rand(2) * 10_000)) # unwanted outlier
fig1, ax1 = plt.subplots()
ax1.boxplot(data)
plt.ylim([-100, 200])
plt.show()

share|improve this answer

answered 2 days ago

rgkrgk

37339

add a comment | 

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0

You can use ylim to constrain the axis without omitting the outliers from the calculation:

data = np.concatenate((np.random.rand(50) * 100, # spread
 np.ones(25) * 50, # center
 np.random.rand(10) * 100 + 100, # flier high
 np.random.rand(10) * -100, # flier low
 np.random.rand(2) * 10_000)) # unwanted outlier
fig1, ax1 = plt.subplots()
ax1.boxplot(data)
plt.ylim([-100, 200])
plt.show()

share|improve this answer

answered 2 days ago

rgkrgk

37339

add a comment | 

0

You can use ylim to constrain the axis without omitting the outliers from the calculation:

data = np.concatenate((np.random.rand(50) * 100, # spread
 np.ones(25) * 50, # center
 np.random.rand(10) * 100 + 100, # flier high
 np.random.rand(10) * -100, # flier low
 np.random.rand(2) * 10_000)) # unwanted outlier
fig1, ax1 = plt.subplots()
ax1.boxplot(data)
plt.ylim([-100, 200])
plt.show()

share|improve this answer

answered 2 days ago

rgkrgk

37339

add a comment | 

You can use ylim to constrain the axis without omitting the outliers from the calculation:

data = np.concatenate((np.random.rand(50) * 100, # spread
 np.ones(25) * 50, # center
 np.random.rand(10) * 100 + 100, # flier high
 np.random.rand(10) * -100, # flier low
 np.random.rand(2) * 10_000)) # unwanted outlier
fig1, ax1 = plt.subplots()
ax1.boxplot(data)
plt.ylim([-100, 200])
plt.show()

share|improve this answer

answered 2 days ago

rgkrgk

37339

data = np.concatenate((np.random.rand(50) * 100, # spread
 np.ones(25) * 50, # center
 np.random.rand(10) * 100 + 100, # flier high
 np.random.rand(10) * -100, # flier low
 np.random.rand(2) * 10_000)) # unwanted outlier
fig1, ax1 = plt.subplots()
ax1.boxplot(data)
plt.ylim([-100, 200])
plt.show()

share|improve this answer

answered 2 days ago

rgkrgk

37339

answered 2 days ago

rgkrgk

37339

answered 2 days ago

rgkrgk

37339