I am trying to create a single dataframe from 50 csv files. I need to use only two columns of the csv files namely 'Date' and 'Close'. I tried using the df.join function inside the for loop, but it eats up a lot of memory and i am getting error "Killed:9" after processing of almost 22-23 csv files.
So, now I am trying to create a list of Dataframes with only 2 columns using the for loop and then I am trying to concat the dfs outside the loop function.
I have following issues to be resolved:-
(i) Though the start date of most of the csv files have start date of 2000-01-01, but there are few csvs which have later start dates. So, I want that the main dataframe should have all the dates, with NaN or empty fields for csv with later start date.
(ii) I want to concat them across the Date as Index.
My code is :-
def compileData(symbol):
with open("nifty50.pickle","rb") as f:
symbols=pickle.load(f)
dfList=[]
main_df=pd.DataFrame()
for symbol in symbols:
df=pd.read_csv('/Users/uditvashisht/Documents/udi_py/stocks/stock_dfs/{}.csv'.format(symbol),infer_datetime_format=True,usecols=['Date','Close'],index_col=None,header=0)
df.rename(columns={'Close':symbol}, inplace=True)
dfList.append(df)
main_df=pd.concat(dfList,axis=1,ignore_index=True,join='outer')
print(main_df.head())
You can use index_col=0 in the read_csv or dflist.append(df.set_index('Date')) to put your Date column in the index of each dataframe. Then using pd.concat with axis=1, Pandas will using intrinsic data alignment to align all dataframes based on the index.
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I am trying to ingest 2 csv files into a single spark dataframe. However, the schema of these 2 datasets is very different, and when I perform the below operation, I get back only the schema of the second csv, as if the first one doesn't exist. How can I solve this? My final goal is to count the total number of words.
paths = ["abfss://lmne.dfs.core.windows.net/csvs/MachineLearning_reddit.csv", "abfss://test1#lmne.dfs.core.windows.net/csvs/bbc_news.csv"]
df0_spark=spark.read.format("csv").option("header","false").load(paths)
df0_spark.write.mode("overwrite").saveAsTable("ML_reddit2")
df0_spark.show()
I tried to load both of the files into a single spark dataframe, but it only gives me back one of the tables.
I have reproduced the above and got the below results.
For sample, I have two csv files in dbfs with different schemas. when I execute the above code, I got the same result.
To get the desired schema enable mergeSchemaand header while reading the files.
Code:
df0_spark=spark.read.format("csv").option("mergeSchema","true").option("header","true").load(paths)
df0_spark.show()
If you want to combine the two files without nulls, we should have a common identity column and we have to read the files individually and use inner join for that.
The solution that has worked for me the best in such cases was to read all distinct files separately, and then union them after they have been put into DataFrames. So your code could look something like this:
paths = ["abfss://lmne.dfs.core.windows.net/csvs/MachineLearning_reddit.csv", "abfss://test1#lmne.dfs.core.windows.net/csvs/bbc_news.csv"]
# Load all distinct CSV files
df1 = spark.read.option("header", false).csv(paths[0])
df2 = spark.read.option("header", false).csv(paths[1])
# Union DataFrames
combined_df = df1.unionByName(df2, allowMissingColumns=True)
Note: if the names of columns differ between the files, then for all columns from first file that are not present in second one, you will have null values. If the schema should be matching, then you can always rename the columns, before the unionByName step.
I have 2 different tabular files, in excel formats. I want to know if an id number from one of the columns in the first excel file (from the "ID" column) exists in the proteome file in a specific column (take "IHD" for example) and if so, to display the value associated with it. Is there a way to do this, specifically in pandas and possible using a for loop?
After loading the excel files with read_excel(), you should merge() the dataframes on ID and protein. This is the recommended approach with pandas rather than looping.
import pandas as pd
clusters = pd.read_excel('clusters.xlsx')
proteins = pd.read_excel('proteins.xlsx')
clusters.merge(proteins, left_on='ID', right_on='protein')
I am new to python and pandas and need help with the following.
I have some data in a dataframe on which I am using groupby function. After using the groupby function I want to access the first row of each group and copy the same to a different dataframe.
For eg.
grouped = com.groupby(['Month'])
Using this will give me groups created month-wise. I just want to extract the first number from all the 12 groups and copy the same row details to another dataframe.
I am trying to iterate through this very large DataFrame that I have in python. The thing is, I only want to pull out data from one specific column that contains the names of a bunch of counties.
I have tried to use iteritems(), itertupel(), and iterrows() to no avail.
Any suggestions on how to do this?
My end goal is to have a nested dictionary with each internal dictionary's key being a name from the DataFrame column.
Also tried to use this method below to select a single column but that will only print the name of the column, not its contents.
for county in map_datafile[['NAME']]:
print(county)
If you delete one pair of square brackets, you get a Series that is iterable:
for county in map_datafile['NAME']:
print(county)
See the difference:
print(type(map_datafile[['NAME']]))
# pandas.core.frame.DataFrame
print(type(map_datafile['NAME']))
# pandas.core.series.Series
I have a loop that is going to create multiple rows of data which I want to convert into a dataframe.
Currently I am creating a CSV format string and inside the loop keep appending to it along separated by a newline. I am creating a CSV file so that I can also save it as a text file for other processing.
File Header:
output_str="Col1,Col2,Col3,Col4\n"
Inside for loop:
output_str += "Val1,Val2,Val3,Val4\n"
I then create an RDD by splitting it with the newline and then convert in into the dataframe as follows.
output_rdd = sc.parallelize(output_str.split("\n"))
output_df = output_rdd.map(lambda x: (x, )).toDF()
It creates a dataframe but only has 1 column. I know that is because of the map function where I am making it into a list with only 1 item in the set. What I need is a list with multiple items. So perhaps I should be calling split() function on every line to get a list. But I am getting a feeling that there should be a much more straight-forward way. Appreciate any help. Thanks.
Edit: To give more information, using Spark SQL I have filtered my dataset to those rows that contain the problem. However the rows contain information in following format (separated by '|'). And I need to extract those values from column 3 which has corresponding flag set to 1 in column 4 (Here it is 0xcd)
Field1|Field2|0xab,0xcd,0xef|0x00,0x01,0x00
So I am collecting the output at the driver and then parsing the last 2 columns after which I am left with regular strings that I want to put back in a dataframe. I am not sure if I can achieve the same using Spark SQL to parse the output in the manner I want.
Yes, indeed your current approach seems a little too complicated... Creating large string in Spark Driver and then parallelizing it with Spark is not really performant.
First of all question from where you are getting your input data? In my opinion you should use one of existing Spark readers to read it. For example you can use:
CSV -> http://spark.apache.org/docs/2.1.0/api/python/pyspark.sql.html#pyspark.sql.DataFrameReader.csv
jdbc -> http://spark.apache.org/docs/2.1.0/api/python/pyspark.sql.html#pyspark.sql.DataFrameReader.jdbc
json -> http://spark.apache.org/docs/2.1.0/api/python/pyspark.sql.html#pyspark.sql.DataFrameReader.json
parquet -> http://spark.apache.org/docs/2.1.0/api/python/pyspark.sql.html#pyspark.sql.DataFrameReader.parquet
not structured text file -> http://spark.apache.org/docs/2.1.0/api/python/pyspark.html#pyspark.SparkContext.textFile
In next step you can preprocess it using Spark DataFrame or RDD API depending on your use case.
A bit late, but currently you're applying a map to create a tuple for each row containing the string as the first element. Instead of this, you probably want to split the string, which can easily be done inside the map step. Assuming all of your rows have the same number of elements you can replace:
output_df = output_rdd.map(lambda x: (x, )).toDF()
with
output_df = output_rdd.map(lambda x: x.split()).toDF()