Is there any easier method to apply trim for only string formatted variables in Pyspark ? Layout has around 500 attributes and I tried below which applies to all. But i want trim to be applied for string only.
for col in result_df.columns:
result_df = result_df.withColumn(col,F.ltrim(F.rtrim(result_df[col])))
Using Functools reduce and dataframe dtypes method:
from functools import reduce
df = sc.parallelize([
(1, 'a ', ' a'),
(1, 'b ', ' b'),
(1, 'c ', ' d'),
(2, 'v ', ' f'),
(3, 'a ', ' e'),
]).toDF(["id", "test1", 'test2'])
str_cols = [col for col, dtype in df.dtypes if dtype=='string']
trim_df = lambda df, col: df.withColumn(col, F.ltrim(F.rtrim(F.col(col))))
new_df = reduce(trim_df, str_cols, df)
Related
I have an RDD like this
[('a', [('d2', 1), ('d1', 1)]),
('addition', [('d2', 1)]),
('administrative', [('d1', 1)]),
('also', [('d1', 1)])]
I want an output to look like
a#d2:1;d1:1
addition#d2:1
administrative#d1:1
also#d1:1
I was trying to remove brackets first in order to achieve the output
rdd_new.map(lambda x: re.sub('\(|\)', '', str(x)))
You can map each rdd entry to a string using the suitable string methods:
result = rdd.map(lambda r: r[0] + '#' + ';'.join(['%s:%d' % (i[0], i[1]) for i in r[1]]))
result.collect()
# ['a#d2:1;d1:1', 'addition#d2:1', 'administrative#d1:1', 'also#d1:1']
I want to know how does LabelEncoder() function.
This is a part of my code
for att in all_features_test:
if (str(test_home_data[att].dtypes) == 'object'):
test_home_data[att].fillna( 'Nothing', inplace = True)
train_home_data[att].fillna( 'Nothing', inplace = True)
train_home_data[att] = LabelEncoder().fit_transform(train_home_data[att])
test_home_data[att] = LabelEncoder().fit_transform(test_home_data[att])
else:
test_home_data[att].fillna( 0, inplace = True)
train_home_data[att].fillna( 0, inplace = True)
Both train and test data set has an attribute 'Condition' which can hold values - Bad, Average and Good
Lets say LabelEncoder() would encode Bad as 0, Average as 2, and Good as 1 in train_home_data. Now would that be same for test_home data?
If not, then what should I do?
You should not label after the split, but before.
The unique labels (= classes) are ordered according to alphabet, see uniques = sorted(set(values)) in this source code snipped from sklearn.preprocessing.LabelEncoder which links to the [source] on the upper right of the page.
python method:
def _encode_python(values, uniques=None, encode=False):
# only used in _encode below, see docstring there for details
if uniques is None:
uniques = sorted(set(values))
uniques = np.array(uniques, dtype=values.dtype)
if encode:
table = {val: i for i, val in enumerate(uniques)}
try:
encoded = np.array([table[v] for v in values])
except KeyError as e:
raise ValueError("y contains previously unseen labels: %s"
% str(e))
return uniques, encoded
else:
return uniques
Same for numpy arrays as classes, see return np.unique(values), because unique() sorts by default:
numpy method:
def _encode_numpy(values, uniques=None, encode=False, check_unknown=True):
# only used in _encode below, see docstring there for details
if uniques is None:
if encode:
uniques, encoded = np.unique(values, return_inverse=True)
return uniques, encoded
else:
# unique sorts
return np.unique(values)
if encode:
if check_unknown:
diff = _encode_check_unknown(values, uniques)
if diff:
raise ValueError("y contains previously unseen labels: %s"
% str(diff))
encoded = np.searchsorted(uniques, values)
return uniques, encoded
else:
return uniques
You can never be sure that the test set and training set have the exactly same classes. The training or testing set might simply lack a class of the three label column 'Condition'.
If you desparately want to encode after the train/test split, you need to check that the number of classes is the same in both sets before the encoding.
Quoting the script:
Uses pure python method for object dtype, and numpy method for all
other dtypes.
python method (object type):
assert sorted(set(train_home_data[att])) == sorted(set(test_home_data[att]))
numpy method (all other types):
assert np.unique(train_home_data[att]) == np.unique(test_home_data[att])
I got the answer for this I guess.
Code
data1 = [('A', 1), ('B', 2),('C', 3) ,('D', 4)]
data2 = [('D', 1), ('A', 2),('A', 3) ,('B', 4)]
df1 = pd.DataFrame(data1, columns = ['col1', 'col2'])
df2 = pd.DataFrame(data2, columns = ['col1', 'col2'])
print(df1['col1'])
print(df2['col1'])
df1['col1'] = LabelEncoder().fit_transform(df1['col1'])
df2['col1'] = LabelEncoder().fit_transform(df2['col1'])
print(df1['col1'])
print(df2['col1'])
Output
0 A
1 B
2 C
3 D
Name: col1, dtype: object # df1
0 D
1 A
2 A
3 B
Name: col1, dtype: object # df2
0 0
1 1
2 2
3 3
Name: col1, dtype: int64 #df1 encoded
0 2
1 0
2 0
3 1
Name: col1, dtype: int64 #df2 encoded
B of df1 is encoded to 1.
and,
B of df2 is encoded to 1 as well
So if I encode training and testing data sets, then the encoded values in training set would reflect in testing data set (only if both are label encoded)
I would suggest fitting the label encoder on one dataset and transforming both:
data1 = [('A', 1), ('B', 2),('C', 3) ,('D', 4)]
data2 = [('D', 1), ('A', 2),('A', 3) ,('B', 4)]
df1 = pd.DataFrame(data1, columns = ['col1', 'col2'])
df2 = pd.DataFrame(data2, columns = ['col1', 'col2'])
# here comes the new code:
le = LabelEncoder()
df1['col1'] = le.fit_transform(df1['col1'])
df2['col1'] = le.transform(df2['col1'])
i have this problem to solve, this is a continuation of a previus question How to iterate over pandas df with a def function variable function and the given answer worked perfectly, but now i have to append all the data in a 2 columns dataframe (Adduct_name and mass).
This is from the previous question:
My goal: i have to calculate the "adducts" for a given "Compound", both represents numbes, but for eah "Compound" there are 46 different "Adducts".
Each adduct is calculated as follow:
Adduct 1 = [Exact_mass*M/Charge + Adduct_mass]
where exact_mass = number, M and Charge = number (1, 2, 3, etc) according to each type of adduct, Adduct_mass = number (positive or negative) according to each adduct.
My data: 2 data frames. One with the Adducts names, M, Charge, Adduct_mass. The other one correspond to the Compound_name and Exact_mass of the Compounds i want to iterate over (i just put a small data set)
Adducts: df_al
import pandas as pd
data = [["M+3H", 3, 1, 1.007276], ["M+3Na", 3, 1, 22.989], ["M+H", 1, 1,
1.007276], ["2M+H", 1, 2, 1.007276], ["M-3H", 3, 1, -1.007276]]
df_al = pd.DataFrame(data, columns=["Ion_name", "Charge", "M", "Adduct_mass"])
Compounds: df
import pandas as pd
data1 = [[1, "C3H64O7", 596.465179], [2, "C30H42O7", 514.293038], [4,
"C44H56O8", 712.397498], [4, "C24H32O6S", 448.191949], [5, "C20H28O3",
316.203834]]
df = pd.DataFrame(data1, columns=["CdId", "Formula", "exact_mass"])
The solution to this problem was:
df_name = df_al["Ion_name"]
df_mass = df_al["Adduct_mass"]
df_div = df_al["Charge"]
df_M = df_al["M"]
#Defining general function
def Adduct(x,i):
return x*df_M[i]/df_div[i] + df_mass[i]
#Applying general function in a range from 0 to 5.
for i in range(5):
df[df_name.loc[i]] = df['exact_mass'].map(lambda x: Adduct(x,i))
Output
Name exact_mass M+3H M+3Na M+H 2M+H M-3H
0 a 596.465179 199.829002 221.810726 597.472455 1193.937634 197.814450
1 b 514.293038 172.438289 194.420013 515.300314 1029.593352 170.423737
2 c 712.397498 238.473109 260.454833 713.404774 1425.802272 236.458557
3 d 448.191949 150.404592 172.386316 449.199225 897.391174 148.390040
4 e 316.203834 106.408554 128.390278 317.211110 633.414944 104.39400
Now that is the rigth calculations but i need now a file where:
-only exists 2 columns (Name and mass)
-All the different adducts are appended one after another
desired out put
Name Mass
a_M+3H 199.82902
a_M+3Na 221.810726
a_M+H 597.472455
a_2M+H 1193.937634
a_M-3H 197.814450
b_M+3H 514.293038
.
.
.
c_M+3H
and so on.
Also i need to combine the name of the respective compound with the ion form (M+3H, M+H, etc).
At this point i have no code for that.
I would apprecitate any advice and a better approach since the begining.
This part is an update of the question above:
Is posible to obtain and ouput like this one:
Name Mass RT
a_M+3H 199.82902 1
a_M+3Na 221.810726 1
a_M+H 597.472455 1
a_2M+H 1193.937634 1
a_M-3H 197.814450 1
b_M+3H 514.293038 3
.
.
.
c_M+3H 2
The RT is the same value for all forms of a compound, in this example is RT for a =1, b = 3, c =2, etc.
Is posible to incorporate (Keep this column) from the data set df (which i update here below)?. As you can see that df has more columns like "Formula" and "RT" which desapear after calculations.
import pandas as pd
data1 = [[a, "C3H64O7", 596.465179, 1], [b, "C30H42O7", 514.293038, 3], [c,
"C44H56O8", 712.397498, 2], [d, "C24H32O6S", 448.191949, 4], [e, "C20H28O3",
316.203834, 1.5]]
df = pd.DataFrame(data1, columns=["Name", "Formula", "exact_mass", "RT"])
Part three! (sorry and thank you)
this is a trial i did on a small data set (df) using the code below, with the same df_al of above.
df=
Code
#Defining variables for calculation
df_name = df_al["Ion_name"]
df_mass = df_al["Adduct_mass"]
df_div = df_al["Charge"]
df_M = df_al["M"]
df_ID= df["Name"]
#Defining the RT dictionary
RT = dict(zip(df["Name"], df["RT"]))
#Removing RT column
df=df.drop(columns=["RT"])
#Defining general function
def Adduct(x,i):
return x*df_M[i]/df_div[i] + df_mass[i]
#Applying general function in a range from 0 to 46.
for i in range(47):
df[df_name.loc[i]] = df['exact_mass'].map(lambda x: Adduct(x,i))
df
output
#Melting
df = pd.melt(df, id_vars=['Name'], var_name = "Adduct", value_name= "Exact_mass", value_vars=[x for x in df.columns if 'Name' not in x and 'exact' not in x])
df['name'] = df.apply(lambda x:x[0] + "_" + x[1], axis=1)
df['RT'] = df.Name.apply(lambda x: RT[x[0]] if x[0] in RT else np.nan)
del df['Name']
del df['Adduct']
df['RT'] = df.name.apply(lambda x: RT[x[0]] if x[0] in RT else np.nan)
df
output
Why NaN?
Here is how I will go about it, pandas.melt comes to rescue:
import pandas as pd
import numpy as np
from io import StringIO
s = StringIO('''
Name exact_mass M+3H M+3Na M+H 2M+H M-3H
0 a 596.465179 199.829002 221.810726 597.472455 1193.937634 197.814450
1 b 514.293038 172.438289 194.420013 515.300314 1029.593352 170.423737
2 c 712.397498 238.473109 260.454833 713.404774 1425.802272 236.458557
3 d 448.191949 150.404592 172.386316 449.199225 897.391174 148.390040
4 e 316.203834 106.408554 128.390278 317.211110 633.414944 104.39400
''')
df = pd.read_csv(s, sep="\s+")
df = pd.melt(df, id_vars=['Name'], value_vars=[x for x in df.columns if 'Name' not in x and 'exact' not in x])
df['name'] = df.apply(lambda x:x[0] + "_" + x[1], axis=1)
del df['Name']
del df['variable']
RT = {'a':1, 'b':2, 'c':3, 'd':5, 'e':1.5}
df['RT'] = df.name.apply(lambda x: RT[x[0]] if x[0] in RT else np.nan)
df
Here is the output:
I'm trying to write data from a Pandas dataframe into a nested hdf5 file, with multiple groups and datasets within each group. I'd like to keep it as a single file which will grow in the future on a daily basis. I've had a go with the following code, which shows the structure of what I'd like to achieve
import h5py
import numpy as np
import pandas as pd
file = h5py.File('database.h5','w')
d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
groups = ['A','B','C']
for m in groups:
group = file.create_group(m)
dataset = ['1','2','3']
for n in dataset:
data = df
ds = group.create_dataset(m + n, data.shape)
print ("Dataset dataspace is", ds.shape)
print ("Dataset Numpy datatype is", ds.dtype)
print ("Dataset name is", ds.name)
print ("Dataset is a member of the group", ds.parent)
print ("Dataset was created in the file", ds.file)
print ("Writing data...")
ds[...] = data
print ("Reading data back...")
data_read = ds[...]
print ("Printing data...")
print (data_read)
file.close()
This way the nested structure is created but it loses the index and columns. I've tried the
df.to_hdf('database.h5', ds, table=True, mode='a')
but didn't work, I get this error
AttributeError: 'Dataset' object has no attribute 'split'
Can anyone shed some light please. Many thanks
df.to_hdf() expects a string as a key parameter (second parameter):
key : string
identifier for the group in the store
so try this:
df.to_hdf('database.h5', ds.name, table=True, mode='a')
where ds.name should return you a string (key name):
In [26]: ds.name
Out[26]: '/A1'
I thought to have a go with pandas\pytables and the HDFStore class instead of h5py. So I tried the following
import numpy as np
import pandas as pd
db = pd.HDFStore('Database.h5')
index = pd.date_range('1/1/2000', periods=8)
df = pd.DataFrame(np.random.randn(8, 3), index=index, columns=['Col1', 'Col2', 'Col3'])
groups = ['A','B','C']
i = 1
for m in groups:
subgroups = ['d','e','f']
for n in subgroups:
db.put(m + '/' + n, df, format = 'table', data_columns = True)
It works, 9 groups (groups instead of datasets in pyatbles instead fo h5py?) created from A/d to C/f. Columns and indexes preserved and can do the dataframe operations I need. Still wondering though whether this is an efficient way to retrieve data from a specific group which will become huge in the the future i.e. operations like
db['A/d'].Col1[4:]
I have an RDD that is populated as
id txt
1 A B C
2 A B C
1 A B C
The result of my word count (pyspark) should be for a combination of string and id associated with it. Example:
[(u'1_A',2), (u'1_B',2), (u'1_C',2),(u'2_A',1),(u'2_B',1),(u'2_C',1)]
I tried using a user defined function to combine id with string splits from text. It, however, complains that the append function is unavailable in this context.
Appreciate any code samples that will set me in the right direction.
Here is an alternative solution using PySpark Dataframe. Mainly, the code uses explode and split to split the txt column. Then, use groupby and count to count number of pairs.
import pyspark.sql.functions as func
rdd = spark.sparkContext.parallelize([(1,'A B C'), (2, 'A B C'), (1,'A B C')])
df = rdd.toDF(['id', 'txt'])
df_agg = df.select('id', func.explode(func.split('txt', ' '))).\
groupby(['id', 'col']).\
count().\
sort(['id', 'col'], ascending=True)
df_agg.rdd.map(lambda x:(str(x['id']) + '_' + x['col'], x['count'] )).collect()
Output
[('1_A', 2), ('1_B', 2), ('1_C', 2), ('2_A', 1), ('2_B', 1), ('2_C', 1)]
the below snippet should work
rdd = sc.parallelize([(1,'A B C'), (2, 'A B C'), (1,'A B C')])
result = rdd \
.map(lambda x: (x[0],x[1].split(' '))) \
.flatMap(lambda x: [ '%s_%s'% (x[0],y) for y in x[1] ]) \
.map(lambda x: (x,1)) \
.reduceByKey(lambda x,y: x + y)
result.collect()
Output
[('1_C', 2), ('1_B', 2), ('1_A', 2), ('2_A', 1), ('2_B', 1), ('2_C', 1)]