I am attempting to create a CSV file using Python by reading from this specific api:
https://dev.socrata.com/foundry/data.cdc.gov/5jp2-pgaw
Where I'm running into trouble is that I would like to specify multiple values of "loc_admin_zip" to search for at once. For example, returning a CSV file where the zip is either "10001" or "10002". However, I can't figure out how to do this, I can only get it to work if "loc_admin_zip" is set to a single value. Any help would be appreciated. My code so far:
import pandas as pd
from sodapy import Socrata
client = Socrata("data.cdc.gov", None)
results = client.get("5jp2-pgaw",loc_admin_zip = 10002)
results_df = pd.DataFrame.from_records(results)
results_df.to_csv('test.csv')
I am new to PySpark and my objective is to use PySpark script in AWS Glue for:
reading a dataframe from input file in Glue => done
changing columns of some rows which satisfy a condition => facing issue
write the updated dataframe on the same schema into S3 => done
The task seems very simple, but I could not find a way to complete it and still facing different different issues with my changing code.
Till now, my code looks like this:
Transform2.printSchema() # input schema after reading
Transform2 = Transform2.toDF()
def updateRow(row):
# my logic to update row based on a global condition
#if row["primaryKey"]=="knownKey": row["otherAttribute"]= None
return row
LocalTransform3 = [] # creating new dataframe from Transform2
for row in Transform2.rdd.collect():
row = row.asDict()
row = updateRow(row)
LocalTransform3.append(row)
print(len(LocalTransform3))
columns = Transform2.columns
Transform3 = spark.createDataFrame(LocalTransform3).toDF(*columns)
print('Transform3 count', Transform3.count())
Transform3.printSchema()
Transform3.show(1,truncate=False)
Transform4 = DynamicFrame.fromDF(Transform3, glueContext, "Transform3")
print('Transform4 count', Transform4.count())
I tried using multiple things like:
using map to update existing rows in a lambda
using collect()
using createDataFrame() to create new dataframe
But faced errors in below steps:
not able to create new updated rdd
not able to create new dataframe from rdd using existing columns
Some errors in Glue I got, at different stages:
ValueError: Some of types cannot be determined after inferring
ValueError: Some of types cannot be determined by the first 100 rows, please try again with sampling
An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob. Traceback (most recent call last):
Any working code snippet or help is appreciated.
from pyspark.sql.functions import col, lit, when
Transform2 = Transform2.toDF()
withKeyMapping = Transform2.withColumn('otherAttribute', when(col("primaryKey") == "knownKey", lit(None)).otherwise(col('otherAttribute')))
This should work for your use-case.
I have a dataframe output as a result of running some code, like so
df = pd.DataFrame({
"i": self.direct_hit_i,
"domain name": self.domain_list,
"j": self.direct_hit_j,
"domain name 2": self.domain_list2,
"domain name cleaned": self.clean_domain_list,
"domain name cleaned 2": self.clean_domain_list2
})
All I was really looking for was a way to save these data to whatever file e.g. txt, csv but in a way where the columns of data align with the header. I was using df.to_csv() with \t delimeter but due to the data have different lengths of string and numbers, the elements within each row never quite line up as a column with the corresponding header. So I resulted to using
with open('./filename.txt', 'w') as fo:
fo.write(df.__repr__())
But bear in mind the data in the dataframe are lists with really long length. So for small lengths it returns
which is exactly what I want. However, when I have very big lists it gives me
So as seen below the outputs are truncated. I would like it to not be truncated since I'll need to manually scroll down and verify things.
Try the syntax:
with open('./filename.txt', 'w') as fo:
fo.write(f'{df!r}')
Another way of doing this export to csv would be to use a too like Mito, which full disclosure I'm the author of. It should allow you to export ot CSV easier than the process here!
So what I'm trying to do is the following:
I have 300+ CSVs in a certain folder. What I want to do is open each CSV and take only the first row of each.
What I wanted to do was the following:
import os
list_of_csvs = os.listdir() # puts all the names of the csv files into a list.
The above generates a list for me like ['file1.csv','file2.csv','file3.csv'].
This is great and all, but where I get stuck is the next step. I'll demonstrate this using pseudo-code:
import pandas as pd
for index,file in enumerate(list_of_csvs):
df{index} = pd.read_csv(file)
Basically, I want my for loop to iterate over my list_of_csvs object, and read the first item to df1, 2nd to df2, etc. But upon trying to do this I just realized - I have no idea how to change the variable being assigned when doing the assigning via an iteration!!!
That's what prompts my question. I managed to find another way to get my original job done no problemo, but this issue of doing variable assignment over an interation is something I haven't been able to find clear answers on!
If i understand your requirement correctly, we can do this quite simply, lets use Pathlib instead of os which was added in python 3.4+
from pathlib import Path
csvs = Path.cwd().glob('*.csv') # creates a generator expression.
#change Path(your_path) with Path.cwd() if script is in dif location
dfs = {} # lets hold the csv's in this dictionary
for file in csvs:
dfs[file.stem] = pd.read_csv(file,nrows=3) # change nrows [number of rows] to your spec.
#or with a dict comprhension
dfs = {file.stem : pd.read_csv(file) for file in Path('location\of\your\files').glob('*.csv')}
this will return a dictionary of dataframes with the key being the csv file name .stem adds this without the extension name.
much like
{
'csv_1' : dataframe,
'csv_2' : dataframe
}
if you want to concat these then do
df = pd.concat(dfs)
the index will be the csv file name.
This sounds like it should be REALLY easy to answer with Google but I'm finding it impossible to answer the majority of my nontrivial pandas/pytables questions this way. All I'm trying to do is to load about 3 billion records from about 6000 different CSV files into a single table in a single HDF5 file. It's a simple table, 26 fields, mixture of strings, floats and ints. I'm loading the CSVs with df = pandas.read_csv() and appending them to my hdf5 file with df.to_hdf(). I really don't want to use df.to_hdf(data_columns = True) because it looks like that will take about 20 days versus about 4 days for df.to_hdf(data_columns = False). But apparently when you use df.to_hdf(data_columns = False) you end up with some pile of junk that you can't even recover the table structure from (or so it appears to my uneducated eye). Only the columns that were identified in the min_itemsize list (the 4 string columns) are identifiable in the hdf5 table, the rest are being dumped by data type into values_block_0 through values_block_4:
table = h5file.get_node('/tbl_main/table')
print(table.colnames)
['index', 'values_block_0', 'values_block_1', 'values_block_2', 'values_block_3', 'values_block_4', 'str_col1', 'str_col2', 'str_col3', 'str_col4']
And any query like df = pd.DataFrame.from_records(table.read_where(condition)) fails with error "Exception: Data must be 1-dimensional"
So my questions are: (1) Do I really have to use data_columns = True which takes 5x as long? I was expecting to do a fast load and then index just a few columns after loading the table. (2) What exactly is this pile of garbage I get using data_columns = False? Is it good for anything if I need my table back with query-able columns? Is it good for anything at all?
This is how you can create an HDF5 file from CSV data using pytables. You could also use a similar process to create the HDF5 file with h5py.
Use a loop to read the CSV files with np.genfromtxt into a np array.
After reading the first CSV file, write the data with .create_table() method, referencing the np array created in Step 1.
For additional CSV files, write the data with .append() method, referencing the np array created in Step 1
End of loop
Updated on 6/2/2019 to read a date field (mm/dd/YYY) and convert to datetime object. Note changes to genfromtxt() arguments! Data used is added below the updated code.
import numpy as np
import tables as tb
from datetime import datetime
csv_list = ['SO_56387241_1.csv', 'SO_56387241_2.csv' ]
my_dtype= np.dtype([ ('a',int),('b','S20'),('c',float),('d',float),('e','S20') ])
with tb.open_file('SO_56387241.h5', mode='w') as h5f:
for PATH_csv in csv_list:
csv_data = np.genfromtxt(PATH_csv, names=True, dtype=my_dtype, delimiter=',', encoding=None)
# modify date in fifth field 'e'
for row in csv_data :
datetime_object = datetime.strptime(row['my_date'].decode('UTF-8'), '%m/%d/%Y' )
row['my_date'] = datetime_object
if h5f.__contains__('/CSV_Data') :
dset = h5f.root.CSV_Data
dset.append(csv_data)
else:
dset = h5f.create_table('/','CSV_Data', obj=csv_data)
dset.flush()
h5f.close()
Data for testing:
SO_56387241_1.csv:
my_int,my_str,my_float,my_exp,my_date
0,zero,0.0,0.00E+00,01/01/1980
1,one,1.0,1.00E+00,02/01/1981
2,two,2.0,2.00E+00,03/01/1982
3,three,3.0,3.00E+00,04/01/1983
4,four,4.0,4.00E+00,05/01/1984
5,five,5.0,5.00E+00,06/01/1985
6,six,6.0,6.00E+00,07/01/1986
7,seven,7.0,7.00E+00,08/01/1987
8,eight,8.0,8.00E+00,09/01/1988
9,nine,9.0,9.00E+00,10/01/1989
SO_56387241_2.csv:
my_int,my_str,my_float,my_exp,my_date
10,ten,10.0,1.00E+01,01/01/1990
11,eleven,11.0,1.10E+01,02/01/1991
12,twelve,12.0,1.20E+01,03/01/1992
13,thirteen,13.0,1.30E+01,04/01/1993
14,fourteen,14.0,1.40E+01,04/01/1994
15,fifteen,15.0,1.50E+01,06/01/1995
16,sixteen,16.0,1.60E+01,07/01/1996
17,seventeen,17.0,1.70E+01,08/01/1997
18,eighteen,18.0,1.80E+01,09/01/1998
19,nineteen,19.0,1.90E+01,10/01/1999