I have a pandas dataframe Bg that was created by taking sample in rows and r for in columns. r is a list of genes that I want to split in a row-wise manner for the entire dataframe.
My code below is taking a long time to run and repeatedly crash. I would like to know if there is a more efficient way to achieve the aim.
import pandas as pd
Bg = pd.DataFrame()
for idx, r in pathway_genes.itertuples():
for i, p in enumerate(M.index):
if idx == p:
for genes, samples in common_mrna.iterrows():
b = pd.DataFrame({r:samples})
Bg = Bg.append(b).fillna(0)
M.index
M.index = ['KEGG_VASOPRESSIN_REGULATED_WATER_REABSORPTION',
'KEGG_DRUG_METABOLISM_OTHER_ENZYMES', 'KEGG_PEROXISOME',
'KEGG_LONG_TERM_POTENTIATION', 'KEGG_ADHERENS_JUNCTION', 'KEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOLISM']
pathway_genes
geneSymbols
KEGG_ABC_TRANSPORTERS
['ABCA1', 'ABCA10', 'ABCA12']
KEGG_ACUTE_MYELOID_LEUKEMIA
['AKT1', 'AKT2', 'AKT3', 'ARAF']
KEGG_ADHERENS_JUNCTION
['ACP1', 'ACTB', 'ACTG1', 'ACTN1', 'ACTN2']
KEGG_ADIPOCYTOKINE_SIGNALING_PATHWAY
['ACACB', 'ACSL1', 'ACSL3', 'ACSL4', 'ACSL5']
KEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOLISM
['ABAT', 'ACY3', 'ADSL', 'ADSS1', 'ADSS2']
common_mrna
common_mrna = pd.DataFrame([[1.2, 1.3, 1.4, 1.5], [1.6,1.7,1.8,1.9], [2.0,2.1,2.2,2.3], [2.4,2.5,2.6,2.7], [2.8,2.9,3.0,3.1],[3.2,3.3,3.4,3.5],[3.6,3.7,3.8,3.9],[4.0,4.1,4.2,4.3],[4.4,4.5,4.6,4.7],[4.8,4.9,5.0,5.1],[5.2,5.3,5.4,5.5],[5.6,5.7,5.8,5.9],[6.0,6.1,6.2,6.3],[6.4,6.5,6.6,6.7],[6.8,6.9,7.0,7.1],[7.2,7.3,7.4,7.5],[7.6,7.7,7.8,7.9]], columns=['TCGA-02-0033-01', 'TCGA-02-2470-01', 'TCGA-02-2483-01', 'TCGA-06-0124-01'], index =['ABCA1','ABCA10','ABCA12','AKT1','AKT2','AKT3','ARAF','ACP1','ACTB','ACTG1','ACTN1','ACTN2','ABAT','ACY3','ADSL','ADSS1','ADSS2'])
Desired output:
Bg = pd.DataFrame([[4.0,4.1,4.2,4.3],[4.4,4.5,4.6,4.7],[4.8,4.9,5.0,5.1],[5.2,5.3,5.4,5.5],[5.6,5.7,5.8,5.9],[6.0,6.1,6.2,6.3],[6.4,6.5,6.6,6.7],[6.8,6.9,7.0,7.1],[7.2,7.3,7.4,7.5],[7.6,7.7,7.8,7.9]], columns=['TCGA-02-0033-01', 'TCGA-02-2470-01', 'TCGA-02-2483-01', 'TCGA-06-0124-01'], index =['ACP1','ACTB','ACTG1','ACTN1','ACTN2','ABAT','ACY3','ADSL','ADSS1','ADSS2'])
Firs of all, you can use list comprehension to match the M_index with the pathway_genes
pathway_genes = {'KEGG_ABC_TRANSPORTERS': ['ABCA1', 'ABCA10', 'ABCA12'], 'KEGG_ACUTE_MYELOID_LEUKEMIA': ['AKT1', 'AKT2', 'AKT3', 'ARAF'], 'KEGG_ADHERENS_JUNCTION': ['ACP1', 'ACTB', 'ACTG1', 'ACTN1', 'ACTN2'], 'KEGG_ADIPOCYTOKINE_SIGNALING_PATHWAY': ['ACACB', 'ACSL1', 'ACSL3', 'ACSL4', 'ACSL5'], 'KEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOLISM': ['ABAT', 'ACY3', 'ADSL', 'ADSS1', 'ADSS2']}
matched_index_symbols = [pathway_genes[i] for i in pathway_genes.keys() if i in M_index]
After that, you can use loc to match all the symbols.
flatten_list = [j for sub in matched_index_symbols for j in sub]
Bg = common_mrna.loc[flatten_list]
Out[26]:
TCGA-02-0033-01 TCGA-02-2470-01 TCGA-02-2483-01 TCGA-06-0124-01
ABCA1 1.2 1.3 1.4 1.5
ABCA10 1.6 1.7 1.8 1.9
ABCA12 2.0 2.1 2.2 2.3
ACP1 4.0 4.1 4.2 4.3
ACTB 4.4 4.5 4.6 4.7
ACTG1 4.8 4.9 5.0 5.1
ACTN1 5.2 5.3 5.4 5.5
ACTN2 5.6 5.7 5.8 5.9
ABAT 6.0 6.1 6.2 6.3
ACY3 6.4 6.5 6.6 6.7
ADSL 6.8 6.9 7.0 7.1
ADSS1 7.2 7.3 7.4 7.5
ADSS2 7.6 7.7 7.8 7.9
Update
It seems that your pathway_genes is not originally a dictionary but a dataframe. If that's the case, you can extract the column index of the dataframe.
pathway_genes
Out[46]:
geneSymbols
KEGG_ABC_TRANSPORTERS [ABCA1, ABCA10, ABCA12]
KEGG_ACUTE_MYELOID_LEUKEMIA [AKT1, AKT2, AKT3, ARAF]
KEGG_ADHERENS_JUNCTION [ACP1, ACTB, ACTG1, ACTN1, ACTN2]
KEGG_ADIPOCYTOKINE_SIGNALING_PATHWAY [ACACB, ACSL1, ACSL3, ACSL4, ACSL5]
KEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOLISM [ABAT, ACY3, ADSL, ADSS1, ADSS2]
matched_index_symbols = np.array([pathway_genes['geneSymbols'].loc[i] for i in pathway_genes.index if i in M_index])
flatten_list = matched_index_symbols.ravel()
Background:
I'm currently developing some data profiling in SQL Server. This consists of calculating aggregate statistics on the values in targeted columns.
I'm using SQL for most of the heavy lifting, but calling Python for some of the statistics that SQL is poor at calculating. I'm leveraging the Pandas package through SQL Server Machine Language Services.
However,
I'm currently developing this script on Visual Studio. The SQL portion is irrelevant other than as background.
Problem:
My issue is that when I call one of the Python statistics functions, it produces the output as a series with the labels seemingly not part of the data. I cannot access the labels at all. I need the values of these labels, and I need to normalize the data and insert a column with static values describing which calculation was performed on that row.
Constraints:
I will need to normalize each statistic so I can union the datasets and pass the values back to SQL for further processing. All output needs to accept dynamic schemas, so no hardcoding labels etc.
Attempted solutions:
I've tried explicitly coercing output to dataframes. This just results in a series with label "0".
I've also tried adding static values to the columns. This just adds the target column name as one of the inaccessible labels, and the intended static value as part of the series.
I've searched many times for a solution, and couldn't find anything relevant to the problem.
Code and results below. Using the iris dataset as an example.
###########################
## AGG STATS TEST SCRIPT
##
###########################
#LOAD MODULES
import pandas as pds
#GET SAMPLE DATASET
iris = pds.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')
#CENTRAL TENDENCY
mode1 = iris.mode()
stat_mode = pds.melt(
mode1
)
stat_median = iris.median()
stat_median['STAT_NAME'] = 'STAT_MEDIAN' #Try to add a column with the value 'STAT_MEDIAN'
#AGGREGATE STATS
stat_describe = iris.describe()
#PRINT RESULTS
print(iris)
print(stat_median)
print(stat_describe)
###########################
## OUTPUT
##
###########################
>>> #PRINT RESULTS
... print(iris) #ORIGINAL DATASET
...
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa
.. ... ... ... ... ...
145 6.7 3.0 5.2 2.3 virginica
146 6.3 2.5 5.0 1.9 virginica
147 6.5 3.0 5.2 2.0 virginica
148 6.2 3.4 5.4 2.3 virginica
149 5.9 3.0 5.1 1.8 virginica
[150 rows x 5 columns]
>>> print(stat_median) #YOU CAN SEE THAT IT INSERTED COLUMN INTO ROW LABELS, VALUE INTO RESULTS SERIES
sepal_length 5.8
sepal_width 3
petal_length 4.35
petal_width 1.3
STAT_NAME STAT_MEDIAN
dtype: object
>>> print(stat_describe) #BASIC DESCRIPTIVE STATS, NEED TO LABEL THE STATISTIC NAMES TO UNPIVOT THIS
sepal_length sepal_width petal_length petal_width
count 150.000000 150.000000 150.000000 150.000000
mean 5.843333 3.057333 3.758000 1.199333
std 0.828066 0.435866 1.765298 0.762238
min 4.300000 2.000000 1.000000 0.100000
25% 5.100000 2.800000 1.600000 0.300000
50% 5.800000 3.000000 4.350000 1.300000
75% 6.400000 3.300000 5.100000 1.800000
max 7.900000 4.400000 6.900000 2.500000
>>>
Any assistance is greatly appreciated. Thank you!
I figured it out. There's a function called reset_index that will convert the index to a column, and create a new numerical index.
stat_median = pds.DataFrame(stat_median)
stat_median.reset_index(inplace=True)
stat_median = stat_median.rename(columns={'index' : 'fieldname', 0: 'value'})
stat_median['stat_name'] = 'median'
I'm trying to figure out if there is a good way to manage units in my pandas data. For example, I have a DataFrame that looks like this:
length (m) width (m) thickness (cm)
0 1.2 3.4 5.6
1 7.8 9.0 1.2
2 3.4 5.6 7.8
Currently, the measurement units are encoded in column names. Downsides include:
column selection is awkward -- df['width (m)'] vs. df['width']
things will likely break if the units of my source data change
If I wanted to strip the units out of the column names, is there somewhere else that the information could be stored?
There isn't any great way to do this right now, see github issue here for some discussion.
As a quick hack, could do something like this, maintaining a separate dict with the units.
In [3]: units = {}
In [5]: newcols = []
...: for col in df:
...: name, unit = col.split(' ')
...: units[name] = unit
...: newcols.append(name)
In [6]: df.columns = newcols
In [7]: df
Out[7]:
length width thickness
0 1.2 3.4 5.6
1 7.8 9.0 1.2
2 3.4 5.6 7.8
In [8]: units['length']
Out[8]: '(m)'
As I was searching for this, too. Here is what pint and the (experimental) pint_pandas is capable of today:
import pandas as pd
import pint
import pint_pandas
ureg = pint.UnitRegistry()
ureg.Unit.default_format = "~P"
pint_pandas.PintType.ureg.default_format = "~P"
df = pd.DataFrame({
"length": pd.Series([1.2, 7.8, 3.4], dtype="pint[m]"),
"width": pd.Series([3.4, 9.0, 5.6], dtype="pint[m]"),
"thickness": pd.Series([5.6, 1.2, 7.8], dtype="pint[cm]"),
})
print(df.pint.dequantify())
length width thickness
unit m m cm
0 1.2 3.4 5.6
1 7.8 9.0 1.2
2 3.4 5.6 7.8
df['width'] = df['width'].pint.to("inch")
print(df.pint.dequantify())
length width thickness
unit m in cm
0 1.2 133.858268 5.6
1 7.8 354.330709 1.2
2 3.4 220.472441 7.8
Offer you some methods:
pands-units-extension: janpipek/pandas-units-extension: Units extension array for pandas based on astropy
pint-pandas: hgrecco/pint-pandas: Pandas support for pint
you can also extend the pandas by yourself following this Extending pandas — pandas 1.3.0 documentation
I have a data frame (df) in pandas with four columns and I want a new column to represent the mean of this four columns: df['mean']= df.mean(1)
1 2 3 4 mean
NaN NaN NaN NaN NaN
5.9 5.4 2.4 3.2 4.225
0.6 0.7 0.7 0.7 0.675
2.5 1.6 1.5 1.2 1.700
0.4 0.4 0.4 0.4 0.400
So far so good. But when I save the results to a csv file this is what I found:
5.9,5.4,2.4,3.2,4.2250000000000005
0.6,0.7,0.7,0.7,0.6749999999999999
2.5,1.6,1.5,1.2,1.7
0.4,0.4,0.4,0.4,0.4
I guess I can force the format in the mean column, but any idea why this is happenning?
I am using winpython with python 3.3.2 and pandas 0.11.0
You could use the float_format parameter:
import pandas as pd
import io
content = '''\
1 2 3 4 mean
NaN NaN NaN NaN NaN
5.9 5.4 2.4 3.2 4.225
0.6 0.7 0.7 0.7 0.675
2.5 1.6 1.5 1.2 1.700
0.4 0.4 0.4 0.4 0.400'''
df = pd.read_table(io.BytesIO(content), sep='\s+')
df.to_csv('/tmp/test.csv', float_format='%g', index=False)
yields
1,2,3,4,mean
,,,,
5.9,5.4,2.4,3.2,4.225
0.6,0.7,0.7,0.7,0.675
2.5,1.6,1.5,1.2,1.7
0.4,0.4,0.4,0.4,0.4
The answers seem correct. Floating point numbers cannot be perfectly represented on our systems. There are bound to be some differences. Read The Floating Point Guide.
>>> a = 5.9+5.4+2.4+3.2
>>> a / 4
4.2250000000000005
As you said, you could always format the results if you want to get only a fixed number of points after the decimal.
>>> "{:.3f}".format(a/4)
'4.225'