I have a Pandas DataFrame, one column, is an OrderedSet like this:
df
OrderedSetCol
0 OrderedSet([1721754, 3622558, 2550234, 2344034, 8550040])
This is:
from ordered_set import OrderedSet
I am just trying to convert this column into list:
df['OrderedSetCol_list'] = df['OrderedSetCol'].apply(lambda x: ast.literal_eval(str("\'" + x.replace('OrderedSet(','').replace(')','') + "\'")))
The code executes succesfully, but, my column type is still str and not list
type(df.loc[0]['OrderedSetCol_list'])
str
What am I doing wrong?
EDIT: My OrderedSetCol is also a string column as I am reading a file from a disk, which was originally saved from OrderedSet column.
Expected Output:
[1721754, 3622558, 2550234, 2344034, 8550040]
You can apply a list calling just like you would do with the OrderedSet itself:
df = pd.DataFrame({'OrderedSetCol':[OrderedSet([1721754, 3622558, 2550234, 2344034, 8550040])]})
df.OrderedSetCol.apply(list)
Output:
[1721754, 3622558, 2550234, 2344034, 8550040]
If your data type string column:
df.OrderedSetCol.str.findall('\d+')
Related
I am brand new to pandas and the parquet file type. I have a python script that:
reads in a hdfs parquet file
converts it to a pandas dataframe
loops through specific columns and changes some values
writes the dataframe back to a parquet file
Then the parquet file is imported back into hdfs using impala-shell.
The issue I'm having appears to be with step 2. I have it print out the contents of the dataframe immediately after it reads it in and before any changes are made in step 3. It appears to be changing the datatypes and the data of some fields, which causes problems when it writes it back to a parquet file. Examples:
fields that show up as NULL in the database are replaced with the string "None" (for string columns) or the string "nan" (for numeric columns) in the printout of the dataframe.
fields that should be an Int with a value of 0 in the database are changed to "0.00000" and turned into a float in the dataframe.
It appears that it is actually changing these values, because when it writes the parquet file and I import it into hdfs and run a query, I get errors like this:
WARNINGS: File '<path>/test.parquet' has an incompatible Parquet schema for column
'<database>.<table>.tport'. Column type: INT, Parquet schema:
optional double tport [i:1 d:1 r:0]
I don't know why it would alter the data and not just leave it as-is. If this is what's happening, I don't know if I need to loop over every column and replace all these back to their original values, or if there is some other way to tell it to leave them alone.
I have been using this reference page:
http://arrow.apache.org/docs/python/parquet.html
It uses
pq.read_table(in_file)
to read the parquet file and then
df = table2.to_pandas()
to convert to a dataframe that I can loop through and change the columns. I don't understand why it's changing the data, and I can't find a way to prevent this from happening. Is there a different way I need to read it than read_table?
If I query the database, the data would look like this:
tport
0
1
My print(df) line for the same thing looks like this:
tport
0.00000
nan
nan
1.00000
Here is the relevant code. I left out the part that processes the command-line arguments since it was long and it doesn't apply to this problem. The file passed in is in_file:
import sys, getopt
import random
import re
import math
import pyarrow.parquet as pq
import numpy as np
import pandas as pd
import pyarrow as pa
import os.path
# <CLI PROCESSING SECTION HERE>
# GET LIST OF COLUMNS THAT MUST BE SCRAMBLED
field_file = open('scrambler_columns.txt', 'r')
contents = field_file.read()
scrambler_columns = contents.split('\n')
def scramble_str(xstr):
#print(xstr + '_scrambled!')
return xstr + '_scrambled!'
parquet_file = pq.ParquetFile(in_file)
table2 = pq.read_table(in_file)
metadata = pq.read_metadata(in_file)
df = table2.to_pandas() #dataframe
print('rows: ' + str(df.shape[0]))
print('cols: ' + str(df.shape[1]))
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', -1)
pd.set_option('display.float_format', lambda x: '%.5f' % x)
#df.fillna(value='', inplace=True) # np.nan # \xa0
print(df) # print before making any changes
cols = list(df)
# https://pythonbasics.org/pandas-iterate-dataframe/
for col_name, col_data in df.iteritems():
#print(cols[index])
if col_name in scrambler_columns:
print('scrambling values in column ' + col_name)
for i, val in col_data.items():
df.at[i, col_name] = scramble_str(str(val))
print(df) # print after making changes
print(parquet_file.num_row_groups)
print(parquet_file.read_row_group(0))
# WRITE NEW PARQUET FILE
new_table = pa.Table.from_pandas(df)
writer = pq.ParquetWriter(out_file, new_table.schema)
for i in range(1):
writer.write_table(new_table)
writer.close()
if os.path.isfile(out_file) == True:
print('wrote ' + out_file)
else:
print('error writing file ' + out_file)
# READ NEW PARQUET FILE
table3 = pq.read_table(out_file)
df = table3.to_pandas() #dataframe
print(df)
EDIT
Here are the datatypes for the 1st few columns in hdfs
and here are the same ones that are in the pandas dataframe:
id object
col1 float64
col2 object
col3 object
col4 float64
col5 object
col6 object
col7 object
It appears to convert
String to object
Int to float64
bigint to float64
How can I tell pandas what data types the columns should be?
Edit 2: I was able to find a workaround by directly processing the pyarrow tables. Please see my question and answers here: How to update data in pyarrow table?
fields that show up as NULL in the database are replaced with the string "None" (for string columns) or the string "nan" (for numeric columns) in the printout of the dataframe.
This is expected. It's just how pandas print function is defined.
It appears to convert String to object
This is also expected. Numpy/pandas does not have a dtype for variable length strings. It's possible to use a fixed-length string type but that would be pretty unusual.
It appears to convert Int to float64
This is also expected since the column has nulls and numpy's int64 is not nullable. If you would like to use Pandas's nullable integer column you can do...
def lookup(t):
if pa.types.is_integer(t):
return pd.Int64Dtype()
df = table.to_pandas(types_mapper=lookup)
Of course, you could create a more fine grained lookup if you wanted to use both Int32Dtype and Int64Dtype, this is just a template to get you started.
Let's say I have a simple pandas dataframe named df :
0 1
0 a [b, c, d]
I save this dataframe into a CSV file as follow :
df.to_csv("test.csv", index=False, sep="\t", encoding="utf-8")
Then later in my script I read this csv :
df = pd.read_csv("test.csv", index_col=False, sep="\t", encoding="utf-8")
Now what I want to do is to use explode() on column '1' but it does not work because the content of column '1' is not a list since I saved df into a CSV file.
What I tried so far is to change column '1' type into a list with astype() without any success.
Thank you by advance.
Try this, Since you are reading from csv file,your dataframe value in column A (1 in your case) is essentially a string for which you need to infer the values as list.
import pandas as pd
import ast
df=pd.DataFrame({"A":["['a','b']","['c']"],"B":[1,2]})
df["A"]=df["A"].apply(lambda x: ast.literal_eval(x))
Now, the following works !
df.explode("A")
def loadCsv(filename):
lines = csv.reader(open('diabetes.csv'))
dataset = list(lines)
for i in range(len(dataset)):
dataset[i] = [float(x) for x in dataset[i]
return dataset
Hello, I'm trying to implement Naive-Bayes but its giving me this error even though i've manually changed the type of each column to float.
it's still giving me error.
Above is the function to convert.
The ValueError is because the code is trying to cast (convert) the items in the CSV header row, which are strings, to floats. You could just skip the first row of the CSV file, for example:
for i in range(1, len(dataset)): # specifying 1 here will skip the first row
dataset[i] = [float(x) for x in dataset[i]
Note: that would leave the first item in dataset as the headers (str).
Personally, I'd use pandas, which has a read_csv() method, which will load the data directly into a dataframe.
For example:
import pandas as pd
dataset = pd.read_csv('diabetes.csv')
This will give you a dataframe though, not a list of lists. If you really want a list of lists, you could use dataset.values.tolist().
I would like to convert a column in dataframe to a string
it looks like this :
company department id family name start_date end_date
abc sales 38221925 Levy nali 16/05/2017 01/01/2018
I want to convert the id from int to string
I tried
data['id']=data['id'].to_string()
and
data['id']=data['id'].astype(str)
got dtype('O')
I expect to receive string
This is intended behaviour. This is how pandas stores strings.
From the docs
Pandas uses the object dtype for storing strings.
For a simple test, you can make a dummy dataframe and check it's dtype too.
import pandas as pd
df = pd.DataFrame(["abc", "ab"])
df[0].dtype
#Output:
dtype('O')
You can do that by using apply() function in this way:
data['id'] = data['id'].apply(lambda x: str(x))
This will convert all the values of id column to string.
You can ensure the type of the values like this:
type(data['id'][0]) (It is checking the first value of 'id' column)
This will give the output str.
And data['id'].dtype will give dtype('O') that is object.
You can also use data.info() to check all the information about that DataFrame.
str(12)
>>'12'
Can easily convert to a String
I use PySpark.
Spark ML's Random Forest output DataFrame has a column "probability" which is a vector with two values. I just want to add two columns to the output DataFrame, "prob1" and "prob2", which correspond to the first and second values in the vector.
I've tried the following:
output2 = output.withColumn('prob1', output.map(lambda r: r['probability'][0]))
but I get the error that 'col should be Column'.
Any suggestions on how to transform a column of vectors into columns of its values?
I figured out the problem with the suggestion above. In pyspark, "dense vectors are simply represented as NumPy array objects", so the issue is with python and numpy types. Need to add .item() to cast a numpy.float64 to a python float.
The following code works:
split1_udf = udf(lambda value: value[0].item(), FloatType())
split2_udf = udf(lambda value: value[1].item(), FloatType())
output2 = randomforestoutput.select(split1_udf('probability').alias('c1'), split2_udf('probability').alias('c2'))
Or to append these columns to the original dataframe:
randomforestoutput.withColumn('c1', split1_udf('probability')).withColumn('c2', split2_udf('probability'))
Got the same problem, below is the code adjusted for the situation when you have n-length vector.
splits = [udf(lambda value: value[i].item(), FloatType()) for i in range(n)]
out = tstDF.select(*[s('features').alias("Column"+str(i)) for i, s in enumerate(splits)])
You may want to use one UDF to extract the first value and another to extract the second. You can then use the UDF with a select call on the output of the random forrest data frame. Example:
from pyspark.sql.functions import udf, col
split1_udf = udf(lambda value: value[0], FloatType())
split2_udf = udf(lambda value: value[1], FloatType())
output2 = randomForrestOutput.select(split1_udf(col("probability")).alias("c1"),
split2_udf(col("probability")).alias("c2"))
This should give you a dataframe output2 which has columns c1 and c2 corresponding to the first and second values in the list stored in the column probability.
I tried #Rookie Boy 's loop but it seems the splits udf loop doesn't work for me.
I modified a bit.
out = df
for i in range(len(n)):
splits_i = udf(lambda x: x[i].item(), FloatType())
out = out.withColumn('{col_}'.format(i), splits_i('probability'))
out.select(*['col_{}'.format(i) for i in range(3)]).show()