How to Calculate Range Between the Dataframe Values Using Python

how to calculate range between the dataframe values using python

df = pd.DataFrame({
    '8. Requirement': ['.685-.695', '.340-.350', '.737-.740', 'foo', '42'],
    '9.Results': [.68, .345, '.739', '.68', 'bar']
})
# or df = pd.read_csv('filename.csv', sep='\t')

df = df.join(df['8. Requirement'].str.extract('(\d*\.?\d+)-(\d*\.?\d+)').rename(columns={0:'min', 1:'max'}))
df['OK'] = pd.to_numeric(df['9.Results'], errors='coerce').between(df['min'].astype(float), df['max'].astype(float))
print(df)

Output:

  8. Requirement 9.Results   min   max     OK
0      .685-.695      0.68  .685  .695  False
1      .340-.350     0.345  .340  .350   True
2      .737-.740      .739  .737  .740   True
3            foo       .68   NaN   NaN  False
4             42       bar   NaN   NaN  False

Calculating the range of values in a Pandas DataFrame using groupby function

High is not in df, please change High with your column

df.groupby("ChildID").apply(lambda x: x['abdomcirc'].max() - x['abdomcirc'].min())

Range of values in Pandas

You could try defining a custom range function, such as:

def calc_range(x):
    return np.max(x) - np.min(x)

and then passing it as a function in agg:

data.groupby("DPI").agg({"SUM_ALL" :["count",pd.Series.mode,"mean","median","min","max", calc_range]})

determine the range of a value using a look up table

You can use a bit of numpy vectorial operations to generate masks, and use them to select your labels:

import numpy as np

a = numbers['number'].values   # numpy array of numbers
r = ranges.set_index('range')  # dataframe of min/max with labels as index

m1 = (a>=r['range_min'].values[:,None]).T  # is number above each min
m2 = (a<r['range_max'].values[:,None]).T   # is number below each max
m3 = (m1&m2)                               # combine both conditions above
# NB. the two operations could be done without the intermediate variables m1/m2

m4 = m3.sum(1)                             # how many matches?
                                           # 0 -> out_of_range
                                           # 2 -> overlap
                                           # 1 -> get column name

# now we select the label according to the conditions
numbers['detected_range'] = np.select([m4==0, m4==2], # out_of_range and overlap
                                      ['out_of_range', 'overlap'],
                                      # otherwise get column name
                                      default=np.take(r.index, m3.argmax(1))
                                     )

output:

   number detected_range
0      50   out_of_range
1      65   out_of_range
2      75              C
3      85              B
4      90        overlap

edit:

It works with any number of intervals in ranges

example output with extra['D',50,51]:

   number detected_range
0      50              D
1      65   out_of_range
2      75              C
3      85              B
4      90        overlap

calculating a range based on the fields of a pandas dataframe

Try this:

d.apply(lambda x: np.arange(x['start'], x['end']+1), axis=1)

Output:

0          [1, 2, 3, 4]
1    [4, 5, 6, 7, 8, 9]
2      [6, 7, 8, 9, 10]
3    [8, 9, 10, 11, 12]
dtype: object

Note: np.arange and range are not designed to accept pd.Series, therefore you can use apply rowwise to create ranges.

How to select a range of values in a pandas dataframe column?

Use between with inclusive=False for strict inequalities:

df['two'].between(-0.5, 0.5, inclusive=False)

The inclusive parameter determines if the endpoints are included or not (True: <=, False: <). This applies to both signs. If you want mixed inequalities, you'll need to code them explicitly:

(df['two'] >= -0.5) & (df['two'] < 0.5)

How can i calculate for Average true range in pandas

It looks like you might be trying to do the following :

import pandas as pd
from numpy.random import rand

df =  pd.DataFrame(rand(10,5),columns={'High-Low','High-close','Low-close','A','B'})

cols = ['High-Low','High-close','Low-close']
df['true_range'] = df[cols].max(axis=1)
print(df)

The output will look like

   High-Low  Low-close         B         A  High-close  true_range
0  0.916121   0.026572  0.082619  0.672000    0.605287    0.916121
1  0.622589   0.944646  0.638486  0.905139    0.262275    0.944646
2  0.611374   0.756191  0.829803  0.828205    0.614956    0.756191
3  0.810638   0.501693  0.504800  0.069532    0.283825    0.810638
4  0.984463   0.900823  0.434061  0.905273    0.518056    0.984463
5  0.377742   0.480266  0.018676  0.383831    0.819448    0.819448
6  0.473753   0.652077  0.730400  0.305507    0.396969    0.652077
7  0.427047   0.733135  0.526076  0.542852    0.719194    0.733135
8  0.911629   0.633997  0.101848  0.020811    0.327233    0.911629
9  0.244624   0.893365  0.278941  0.354696    0.678280    0.893365

If this isn't what you had in mind, it would be helpful to clarify your question by providing a small example where you clearly identify the columns and the index in your DataFrame and what you mean by "true range".

Pandas / python to autogenerate new values through a range

Here's one way to do it:

import pandas as pd
df = pd.DataFrame({
    'value' : ['a', 'b'],
    'range1' : [0, 4],
    'range2' : [3, 6],
    'color' : ['blue', 'yellow']
})
df['output'] = df.apply(lambda x: [x['value'] + str(i + 1) for i in range(x['range1'], x['range2'])], axis=1)
df = df.explode('output', ignore_index=True)[['output', 'value', 'color']]
print(df)

Output

  output value   color
0     a1     a    blue
1     a2     a    blue
2     a3     a    blue
3     b5     b  yellow
4     b6     b  yellow

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