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I have successfully read a csv file using pandas. When I am trying to print the a particular column from the data frame i am getting keyerror. Hereby i am sharing the code with the error.
import pandas as pd
reviews_new = pd.read_csv("D:\\aviva.csv")
reviews_new['review']
**
reviews_new['review']
Traceback (most recent call last):
File "<ipython-input-43-ed485b439a1c>", line 1, in <module>
reviews_new['review']
File "C:\Users\30216\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\frame.py", line 1997, in __getitem__
return self._getitem_column(key)
File "C:\Users\30216\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\frame.py", line 2004, in _getitem_column
return self._get_item_cache(key)
File "C:\Users\30216\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\generic.py", line 1350, in _get_item_cache
values = self._data.get(item)
File "C:\Users\30216\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\internals.py", line 3290, in get
loc = self.items.get_loc(item)
File "C:\Users\30216\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\indexes\base.py", line 1947, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas\index.pyx", line 137, in pandas.index.IndexEngine.get_loc (pandas\index.c:4154)
File "pandas\index.pyx", line 159, in pandas.index.IndexEngine.get_loc (pandas\index.c:4018)
File "pandas\hashtable.pyx", line 675, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12368)
File "pandas\hashtable.pyx", line 683, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12322)
KeyError: 'review'
**
Can someone help me in this ?
I think first is best investigate, what are real columns names, if convert to list better are seen some whitespaces or similar:
print (reviews_new.columns.tolist())
I think there can be 2 problems (obviously):
1.whitespaces in columns names (maybe in data also)
Solutions are strip whitespaces in column names:
reviews_new.columns = reviews_new.columns.str.strip()
Or add parameter skipinitialspace to read_csv:
reviews_new = pd.read_csv("D:\\aviva.csv", skipinitialspace=True)
2.different separator as default ,
Solution is add parameter sep:
#sep is ;
reviews_new = pd.read_csv("D:\\aviva.csv", sep=';')
#sep is whitespace
reviews_new = pd.read_csv("D:\\aviva.csv", sep='\s+')
reviews_new = pd.read_csv("D:\\aviva.csv", delim_whitespace=True)
EDIT:
You get whitespace in column name, so need 1.solutions:
print (reviews_new.columns.tolist())
['Name', ' Date', ' review']
^ ^
import pandas as pd
df=pd.read_csv("file.txt", skipinitialspace=True)
df.head()
df['review']
dfObj['Hash Key'] = (dfObj['DEAL_ID'].map(str) +dfObj['COST_CODE'].map(str) +dfObj['TRADE_ID'].map(str)).apply(hash)
#for index, row in dfObj.iterrows():
# dfObj.loc[`enter code here`index,'hash'] = hashlib.md5(str(row[['COST_CODE','TRADE_ID']].values)).hexdigest()
print(dfObj['hash'])
I have below code that reads from a csv file a number of ticker symbols into a dataframe.
Each ticker calls the Web Api returning a dafaframe df which is then attached to the last one until complete. The code works , but when a large number of tickers is used the code slows down tremendously. I understand I can use multiprocessing and threads to speed up my code but dont know where to start and what would be the most suited in my particular case.
What code should I use to get my data into a combined daframe in the fastest possible manner?
import pandas as pd
import numpy as np
import json
tickers=pd.read_csv("D:/verhuizen/pensioen/MULTI.csv",names=['symbol','company'])
read_str='https://financialmodelingprep.com/api/v3/income-statement/AAPL?limit=120&apikey=demo'
df = pd.read_json (read_str)
df = pd.DataFrame(columns=df.columns)
for ind in range(len(tickers)):
read_str='https://financialmodelingprep.com/api/v3/income-statement/'+ tickers['symbol'][ind] +'?limit=120&apikey=demo'
df1 = pd.read_json (read_str)
df=pd.concat([df,df1], ignore_index=True)
df.set_index(['date','symbol'], inplace=True)
df.sort_index(inplace=True)
df.to_csv('D:/verhuizen/pensioen/MULTI_out.csv')
The code provided works fine for smaller data sets, but when I use a large number of tickers (>4,000) at some point I get the below error. Is this because the web api gets overloaded or is there another problem?
Traceback (most recent call last):
File "D:/Verhuizen/Pensioen/Equity_Extractor_2021.py", line 43, in <module>
data = pool.starmap(download_data, enumerate(TICKERS, start=1))
File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 276, in starmap
return self._map_async(func, iterable, starmapstar, chunksize).get()
File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 657, in get
raise self._value
multiprocessing.pool.MaybeEncodingError: Error sending result: '<multiprocessing.pool.ExceptionWithTraceback object at 0x00C33E30>'. Reason: 'TypeError("cannot serialize '_io.BufferedReader' object")'
Process finished with exit code 1
It keeps giving the same error (for a larger amount of tickers)
code is exactly as provided:
def download_data(pool_id, symbols):
df = []
for symbol in symbols:
print("[{:02}]: {}".format(pool_id, symbol))
#do stuff here
read_str = BASEURL.format(symbol)
df.append(pd.read_json(read_str))
#df.append(pd.read_json(fake_data(symbol)))
return pd.concat(df, ignore_index=True)
It failed again with the pool.map, but one strange thing I noticed. Each time it fails it does so around 12,500 tickers (total is around 23,000 tickers) Similar error:
Traceback (most recent call last):
File "C:/Users/MLUY/AppData/Roaming/JetBrains/PyCharmCE2020.1/scratches/Equity_naive.py", line 21, in <module>
data = pool.map(download_data, TICKERS)
File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 268, in map
return self._map_async(func, iterable, mapstar, chunksize).get()
File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 657, in get
raise self._value
multiprocessing.pool.MaybeEncodingError: Error sending result: '<multiprocessing.pool.ExceptionWithTraceback object at 0x078D1BF0>'. Reason: 'TypeError("cannot serialize '_io.BufferedReader' object")'
Process finished with exit code 1
I get the tickers also from a API call https://financialmodelingprep.com/api/v3/financial-statement-symbol-lists?apikey=demo (I noticed it does not work without subscription), I wanted to attach the data it as a csv file but I dont have sufficient rights. I dont think its a good idea to paste the returned data here...
I tried adding time.sleep(0.2) before return as suggested, but again I ge the same error at ticker 12,510. Strange everytime its around the same location. As there are multiple processes going on I cannot see at what point its breaking
Traceback (most recent call last):
File "C:/Users/MLUY/AppData/Roaming/JetBrains/PyCharmCE2020.1/scratches/Equity_naive.py", line 24, in <module>
data = pool.map(download_data, TICKERS)
File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 268, in map
return self._map_async(func, iterable, mapstar, chunksize).get()
File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 657, in get
raise self._value
multiprocessing.pool.MaybeEncodingError: Error sending result: '<multiprocessing.pool.ExceptionWithTraceback object at 0x00F32C90>'. Reason: 'TypeError("cannot serialize '_io.BufferedReader' object")'
Process finished with exit code 1
Something very very strange is going on , I have split the data in chunks of 10,000 / 5,000 / 4,000 and 2,000 and each time the code breaks approx 100 tickers from the end. Clearly there is something going on that not right
import time
import pandas as pd
import multiprocessing
# get tickers from your csv
df=pd.read_csv('D:/Verhuizen/Pensioen/All_Symbols.csv',header=None)
# setting the Dataframe to a list (in total 23,000 tickers)
df=df[0]
TICKERS=df.tolist()
#Select how many tickers I want
TICKERS=TICKERS[0:2000]
BASEURL = "https://financialmodelingprep.com/api/v3/income-statement/{}?limit=120&apikey=demo"
def download_data(symbol):
print(symbol)
# do stuff here
read_str = BASEURL.format(symbol)
df = pd.read_json(read_str)
#time.sleep(0.2)
return df
if __name__ == "__main__":
with multiprocessing.Pool(multiprocessing.cpu_count()) as pool:
data = pool.map(download_data, TICKERS)
df = pd.concat(data).set_index(["date", "symbol"]).sort_index()
df.to_csv('D:/verhuizen/pensioen/Income_2000.csv')
In this particular example the code breaks at position 1,903
RPAI
Traceback (most recent call last):
File "C:/Users/MLUY/AppData/Roaming/JetBrains/PyCharmCE2020.1/scratches/Equity_testing.py", line 27, in <module>
data = pool.map(download_data, TICKERS)
File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 268, in map
return self._map_async(func, iterable, mapstar, chunksize).get()
File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 657, in get
raise self._value
multiprocessing.pool.MaybeEncodingError: Error sending result: '<multiprocessing.pool.ExceptionWithTraceback object at 0x0793EAF0>'. Reason: 'TypeError("cannot serialize '_io.BufferedReader' object")'
First optimization is to avoid concatenate your dataframe at each iteration.
You can try something like that:
url = "https://financialmodelingprep.com/api/v3/income-statement/{}?limit=120&apikey=demo"
df = []
for symbol in tickers["symbol"]:
read_str = url.format(symbol)
df.append(pd.read_json(read_str))
df = pd.concat(df, ignore_index=True)
If it's not sufficient, we will see to use async, threading or multiprocessing.
Edit:
The code below can do the job:
import pandas as pd
import numpy as np
import multiprocessing
import time
import random
PROCESSES = 4 # number of parallel process
CHUNKS = 6 # one process handle n symbols
# get tickers from your csv
TICKERS = ["BCDA", "WBAI", "NM", "ZKIN", "TNXP", "FLY", "MYSZ", "GASX", "SAVA", "GCE",
"XNET", "SRAX", "SINO", "LPCN", "XYF", "SNSS", "DRAD", "WLFC", "OILD", "JFIN",
"TAOP", "PIC", "DIVC", "MKGI", "CCNC", "AEI", "ZCMD", "YVR", "OCG", "IMTE",
"AZRX", "LIZI", "ORSN", "ASPU", "SHLL", "INOD", "NEXI", "INR", "SLN", "RHE-PA",
"MAX", "ARRY", "BDGE", "TOTA", "PFMT", "AMRH", "IDN", "OIS", "RMG", "IMV",
"CHFS", "SUMR", "NRG", "ULBR", "SJI", "HOML", "AMJL", "RUBY", "KBLMU", "ELP"]
# create a list of n sublist
TICKERS = [TICKERS[i:i + CHUNKS] for i in range(0, len(TICKERS), CHUNKS)]
BASEURL = "https://financialmodelingprep.com/api/v3/income-statement/{}?limit=120&apikey=demo"
def fake_data(symbol):
dti = pd.date_range("1985", "2020", freq="Y")
df = pd.DataFrame({"date": dti, "symbol": symbol,
"A": np.random.randint(0, 100, size=len(dti)),
"B": np.random.randint(0, 100, size=len(dti))})
time.sleep(random.random()) # to simulate network delay
return df.to_json()
def download_data(pool_id, symbols):
df = []
for symbol in symbols:
print("[{:02}]: {}".format(pool_id, symbol))
# do stuff here
# read_str = BASEURL.format(symbol)
# df.append(pd.read_json(read_str))
df.append(pd.read_json(fake_data(symbol)))
return pd.concat(df, ignore_index=True)
if __name__ == "__main__":
with multiprocessing.Pool(PROCESSES) as pool:
data = pool.starmap(download_data, enumerate(TICKERS, start=1))
df = pd.concat(data).set_index(["date", "symbol"]).sort_index()
In this example, I split the list of tickers into sublists for each process retrieves data for multiple symbols and limits overhead due to create and destroy processes.
The delay is to simulate the response time from the network connection and highlight the multiprocess behaviour.
Edit 2: simpler but naive version for your needs
import pandas as pd
import multiprocessing
# get tickers from your csv
TICKERS = ["BCDA", "WBAI", "NM", "ZKIN", "TNXP", "FLY", "MYSZ", "GASX", "SAVA", "GCE",
"XNET", "SRAX", "SINO", "LPCN", "XYF", "SNSS", "DRAD", "WLFC", "OILD", "JFIN",
"TAOP", "PIC", "DIVC", "MKGI", "CCNC", "AEI", "ZCMD", "YVR", "OCG", "IMTE",
"AZRX", "LIZI", "ORSN", "ASPU", "SHLL", "INOD", "NEXI", "INR", "SLN", "RHE-PA",
"MAX", "ARRY", "BDGE", "TOTA", "PFMT", "AMRH", "IDN", "OIS", "RMG", "IMV",
"CHFS", "SUMR", "NRG", "ULBR", "SJI", "HOML", "AMJL", "RUBY", "KBLMU", "ELP"]
BASEURL = "https://financialmodelingprep.com/api/v3/income-statement/{}?limit=120&apikey=demo"
def download_data(symbol):
print(symbol)
# do stuff here
read_str = BASEURL.format(symbol)
df = pd.read_json(read_str)
return df
if __name__ == "__main__":
with multiprocessing.Pool(multiprocessing.cpu_count()) as pool:
data = pool.map(download_data, TICKERS)
df = pd.concat(data).set_index(["date", "symbol"]).sort_index()
Note about pool.map: for each symbol in TICKERS, create a process and call function download_data.
I have a Pandas dataframe with a column that contains a list of dict/structs. One of the keys (thing in the example below) can have a value that is either an int or a string. Is there a way to define a PyArrow type that will allow this dataframe to be converted into a PyArrow table, for eventual output to a Parquet file?
I tried using pa.union for this, but I seem to be doing something not supported/implemented.
import pandas as pd
import pyarrow as pa
df = pd.DataFrame(data={"id": [1, 2], "dict": [{"thing": 1}, {"thing": "two"}]})
schema = pa.schema([
pa.field("id", pa.int64()),
pa.field("dict", pa.struct([
("thing", pa.union([
pa.field("int64", pa.int64()),
pa.field("string", pa.string()),
], "sparse"))
]))
])
t = pa.Table.from_pandas(df, schema=schema)
Result:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pyarrow/table.pxi", line 1394, in pyarrow.lib.Table.from_pandas
File "/usr/local/lib/python3.8/site-packages/pyarrow/pandas_compat.py", line 587, in dataframe_to_arrays
arrays = [convert_column(c, f)
File "/usr/local/lib/python3.8/site-packages/pyarrow/pandas_compat.py", line 587, in <listcomp>
arrays = [convert_column(c, f)
File "/usr/local/lib/python3.8/site-packages/pyarrow/pandas_compat.py", line 574, in convert_column
raise e
File "/usr/local/lib/python3.8/site-packages/pyarrow/pandas_compat.py", line 568, in convert_column
result = pa.array(col, type=type_, from_pandas=True, safe=safe)
File "pyarrow/array.pxi", line 292, in pyarrow.lib.array
File "pyarrow/array.pxi", line 83, in pyarrow.lib._ndarray_to_array
File "pyarrow/error.pxi", line 105, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: ('sparse_union', 'Conversion failed for column dict with type object')
The help text for pa.union doesn't give an example of how to use it.
>>> help(pa.union)
Help on built-in function union in module pyarrow.lib:
union(...)
union(children_fields, mode, type_codes=None)
Create UnionType from children fields.
A union is defined by an ordered sequence of types; each slot in the union
can have a value chosen from these types.
Parameters
----------
fields : sequence of Field values
Each field must have a UTF8-encoded name, and these field names are
part of the type metadata.
mode : str
Either 'dense' or 'sparse'.
type_codes : list of integers, default None
Returns
-------
type : DataType
It looks like it's not implemented yet in pyarrow 2.0.0:
import pandas as pd
import pyarrow as pa
union = pa.union([
pa.field("int64", pa.int64()),
pa.field("string", pa.string()),
], 'sparse')
pa.array([1, 'two'], union)
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-72-f7ec6792b124> in <module>
10 ], 'sparse')
11
---> 12 pa.array([1, 'two'], union)
/nix/store/aagq4nyc9m4ikjda1mykgv125v792zk7-python3-3.7.7-env/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.array()
/nix/store/aagq4nyc9m4ikjda1mykgv125v792zk7-python3-3.7.7-env/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()
/nix/store/aagq4nyc9m4ikjda1mykgv125v792zk7-python3-3.7.7-env/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
/nix/store/aagq4nyc9m4ikjda1mykgv125v792zk7-python3-3.7.7-env/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: sparse_union
PyArrow has a built in method .from_pandas()
https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.from_pandas
import pandas as pd
import pyarrow as pa
df = pd.DataFrame({
... 'int': [1, 2],
... 'str': ['a', 'b']
... })
pa.Table.from_pandas(df)
<pyarrow.lib.Table object at 0x7f05d1fb1b40>
I need to find people with greater or equal to threshold gain =>
dataframe contains column 'capitalGain' with different values 10,20,30,50,1000,5000,10000 ...etc
I try :
Function:
def get_num_people_with_higher_gain(dataframe, threshold_gain)
threshold_gain = dataframe["capitalGain"][dataframe["capitalGain"] >= threshold_gain].count()
return threshold_gain
Call function
df = get_num_people_with_higher_gain(dataframe, threshold_gain)
But I get the following error message:
NameError Traceback (most recent call last)
<ipython-input-50-5485c90412c8> in <module>
----> 1 df = get_num_people_with_higher_gain(dataframe, threshold_gain)
2 threshold = get_num_people_with_higher_gain(dataframe, threshold_gain)
NameError: name 'dataframe' is not defined
Since there are 2 arguments in the function (dataframe, threshold_gain), does it mean that both should be somehow defined within the function ?
Thanks
Finally,
Here is the solution
def get_num_people_with_higher_gain(dataframe, threshold_gain):
result = len(dataframe[dataframe["capitalGain"] >= threshold_gain])
return result
result = get_num_people_with_higher_gain(dataframe,60000)
result
I have a dataframe as follows
Occupation, Genre, Rating
I have taken sum of all rating as totalRating. Now I want to create neeew column w_rating which take (rating >3)/totalRating for particular Occupation,Genre Combination. My dataframe name is joinedRDD so i amwriting below query
resultDF = joinedDF3.groupby([joinedDF3["Occupation"],joinedDF3["Genre"]]).withColumn(wa_rating, sum(Rating>3)/totalRating).collect()
but it is showing error
AttributeError: 'GroupedData' object has no attribute 'withColumn'
So it is clear from error that we cannot use withColumn with groupby
So my question is how to do it?
Below is my updated code.
from pyspark.sql import SparkSession
from pyspark.sql.types import (StructField,StructType,IntegerType,StringType)
from pyspark.sql import Row
from pyspark.sql.functions import sum
import pyspark.sql.functions as F
from pyspark.sql.functions import lit
spark = SparkSession.builder.appName("Movielens Analysis").getOrCreate()
def refineMovieDF(row):
genre=[]
movieData =row[0].split("|")
for i in range(len(movieData)-5):
if int(movieData[i+5]) ==1:
genre.append((int(movieData[0]),i))
return genre
ratingSchema =StructType(fields=[StructField("UserId",IntegerType(),True),StructField("MovieId",IntegerType(),True),StructField("Rating",IntegerType(),True),StructField("TimeStamp",IntegerType(),True)])
ratingsDF = spark.read.load("ml-100k/u.data", format="csv",sep="\t", inferSchema=True, header=False,schema=ratingSchema)
genreSchema =StructType(fields=[StructField("Genre",StringType(),True),StructField("GenreId",IntegerType(),True)])
genreDF = spark.read.load("ml-100k/u.genre",format="csv",sep="|",inferSchema=True, header=False,schema=genreSchema)
userSchema =StructType(fields=[StructField("UserId",IntegerType(),True),StructField("Age",IntegerType(),True),StructField("Gender",StringType(),True),StructField("Occupation",StringType(),True),StructField("ZipCode",IntegerType(),True)])
usersDF = spark.read.load("ml-100k/u.user",format="csv",sep="|",inferSchema=True, header=False,schema=userSchema)
movieSchema =StructType(fields=[StructField("MovieRow",StringType(),True)])
movieDF = spark.read.load("ml-100k/u.item",format="csv",inferSchema=True, header=False,schema=movieSchema)
movieRefinedRDD = movieDF.rdd.flatMap(refineMovieDF)
movieSchema =StructType(fields=[StructField("MovieId",IntegerType(),True),StructField("GenreId",IntegerType(),True)])
movieRefinedDf = spark.createDataFrame(movieRefinedRDD, movieSchema)
joinedDF1 = ratingsDF.join(usersDF,ratingsDF.UserId==usersDF.UserId).select(usersDF["Occupation"],ratingsDF["Rating"],ratingsDF["MovieId"])
joinedDF3 = joinedDF1.join(joinedDF2,joinedDF1.MovieId == joinedDF2.MovieId).select(joinedDF1["Occupation"],joinedDF1["Rating"],joinedDF2["Genre"])
totalRating = joinedDF3.groupBy().sum("Rating").collect()
resultDF = joinedDF3.groupby([joinedDF3["Occupation"],joinedDF3["Genre"]]).agg((sum(joinedDF3["Rating"]>3)/totalRating).alias(wa_rating)).collect()
print(resultDF)
Now I am getting below error.
2019-08-06 22:24:20 INFO BlockManagerInfo:54 - Removed broadcast_11_piece0 on 10.0.2.15:58903 in memory (size: 4.3 KB, free: 413.8 MB)
Traceback (most recent call last):
File "/home/cloudera/workspace/MovielensAnalysis.py", line 59, in <module>
resultDF = joinedDF3.groupby([joinedDF3["Occupation"],joinedDF3["Genre"]]).agg((sum(joinedDF3["Rating"]>3)/totalRating).alias(wa_rating)).collect()
File "/usr/local/spark/python/lib/pyspark.zip/pyspark/sql/column.py", line 116, in _
File "/usr/local/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
File "/usr/local/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
File "/usr/local/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o129.divide.: java.lang.RuntimeException: Unsupported literal type class java.util.ArrayList [[572536]]