I have a dataframe with column which contains two different column values and their name as follows:
How Do I transform it into separate columns?
So far, I tried Following:
use df[col].apply(pd.Series) - It didn't work since data in column is not in dictionary format.
Tried separating columns by a semi-colon (";") sign but It is not a good idea since the given dataframe might have n number of column based on response.
EDIT:
Data in plain text format:
d = {'ClusterName': ['Date:20191010;Bucket:All','Date:20191010;Bucket:some','Date:20191010;Bucket:All']}
How about:
df2 = (df["ClusterName"]
.str.replace("Date:", "")
.str.replace("Bucket:", "")
.str.split(";", expand=True))
df2.columns = ["Date", "Bucket"]
EDIT:
Without hardcoding the variable names, here's a quick hack. You can clean it up (and make less silly variable names):
df_temp = df.ClusterName.str.split(";", expand=True)
cols = []
for col in df_temp:
df_temptemp = df_temp[col].str.split(":", expand=True)
df_temp[col] = df_temptemp[1]
cols.append(df_temptemp.iloc[0, 0])
df_temp.columns = cols
So .. maybe like this ...
Setup the data frame
d = {'ClusterName': ['Date:20191010;Bucket:All','Date:20191010;Bucket:some','Date:20191010;Bucket:All']}
df = pd.DataFrame(data=d)
df
Parse over the dataframe breaking apart by colon and semi-colon
ls = []
for index, row in df.iterrows():
splits = row['ClusterName'].split(';')
print(splits[0].split(':')[1],splits[1].split(':')[1])
ls.append([splits[0].split(':')[1],splits[1].split(':')[1]])
df = pd.DataFrame(ls, columns =['Date', 'Bucket'])
Related
My csv file has below columns:
AFM_reversal_indicator,Alert_Message,axiom_key,_timediff,player,__mv_Splunk_Alert_Id,__mv_nbr_plastic,__mv_code_acct_stat_demo.
I want to remove columns starting with "__mv".
I saw some posts where pandas are used to filter out columns.
Is it possible to do it using csv module in python, If yes how ?
Also, with Pandas what regex should I give:
df.filter(regex='')
df.to_csv(output_file_path)
P.S I am using python3.8
You mean with standard python? You can use a list comprehension, e.g.
import csv
with open( 'data.csv', 'r' ) as f:
DataGenerator = csv.reader( f )
Header = next( DataGenerator )
Header = [ Col.strip() for Col in Header ]
Data = list( DataGenerator )
if Data[-1] == []: del( Data[-1] )
Data = [ [Row[i] for i in range( len( Header ) ) if not Header[i].startswith( "__mv" ) ] for Row in Data ]
Header = [ Col for Col in Header if not Col.startswith( "__mv" ) ]
However, this is just a simple example. You'll probably have further things to consider, e.g. what type your csv columns have, whether you want to read all the data at once like I do here, or one-by-one from the generator to save on memory, etc.
You could also use the builtin filter command instead of the inner list comprehension.
Also, if you have numpy installed and you wanted something more 'numerical', you can always use "structured numpy arrays" (https://numpy.org/doc/stable/user/basics.rec.html). They're quite nice. (personally I prefer them to pandas anyway). Numpy also has its own csv-reading functions (see: https://www.geeksforgeeks.org/how-to-read-csv-files-with-numpy/)
You don't need to use .filter for that. You can just find out which are those columns and then drop them from the DataFrame
import pandas as pd
# Load the dataframe (In our case create a dummy one with your columns)
df = pd.DataFrame(columns = ["AFM_reversal_indicator", "Alert_Message,axiom_key", "_timediff,player", "__mv_Splunk_Alert_Id", "__mv_nbr_plastic", "__mv_code_acct_stat_demo"])
# Get a list of all column names starting with "__mv"
mv_columns = [col for col in df.columns if col.startswith("__mv")]
# Drop the columns
df = df.drop(columns=mv_columns)
# Save the updated dataframe to a CSV file
df.to_csv("cleaned_data.csv", index=False)
The mv_columns will iterate through the columns in your DataFrame and pick those that starts with "__mv". Then the .drop will just remove those from it.
If for some reason you want to use csv package only, then the solution might not be as elegant as with pandas. But here is a suggestion:
import csv
with open("original_data.csv", "r") as input_file, open("cleaned_data.csv", "w", newline="") as output_file:
reader = csv.reader(input_file)
writer = csv.writer(output_file)
header_row = next(reader)
mv_columns = [col for col in header_row if col.startswith("__mv")]
mv_column_indices = [header_row.index(col) for col in mv_columns]
new_header_row = [col for col in header_row if col not in mv_columns]
writer.writerow(new_header_row)
for row in reader:
new_row = [row[i] for i in range(len(row)) if i not in mv_column_indices]
writer.writerow(new_row)
So, first, you read the first row that supposed to be your headers. With a similar logic as before, you find those columns that starts with "__mv" and then you get their indices. You write the new columns to your output file with those columns that don't exist to the "__mv" columns. Then you need to iterate through the rest of the CSV and remove those columns as you go.
My current dataframe looks as below:
existing_data = {'STORE_ID': ['1234','5678','9876','3456','6789'],
'FULFILLMENT_TYPE': ['DELIVERY','DRIVE','DELIVERY','DRIVE','DELIVERY'],
'FORECAST_DATE':['2020-08-01','2020-08-02','2020-08-03','2020-08-04','2020-08-05'],
'DAY_OF_WEEK':['SATURDAY','SUNDAY','MONDAY','TUESDAY','WEDNESDAY'],
'START_HOUR':[8,8,6,7,9],
'END_HOUR':[19,19,18,19,17]}
existing = pd.DataFrame(data=existing_data)
I would need the data to be exploded between the start and end hour such that each hour is a different row like below:
needed_data = {'STORE_ID': ['1234','1234','1234','1234','1234'],
'FULFILLMENT_TYPE': ['DELIVERY','DELIVERY','DELIVERY','DELIVERY','DELIVERY'],
'FORECAST_DATE':['2020-08-01','2020-08-01','2020-08-01','2020-08-01','2020-08-01'],
'DAY_OF_WEEK': ['SATURDAY','SATURDAY','SATURDAY','SATURDAY','SATURDAY'],
'HOUR':[8,9,10,11,12]}
required = pd.DataFrame(data=needed_data)
Not sure how to achieve this ..I know it should be with explode() but unable to achieve it.
If small DataFrame or performance is not important use range per both columns with DataFrame.explode:
existing['HOUR'] = existing.apply(lambda x: range(x['START_HOUR'], x['END_HOUR']+1), axis=1)
existing = (existing.explode('HOUR')
.reset_index(drop=True)
.drop(['START_HOUR','END_HOUR'], axis=1))
If performance is important use Index.repeat by subtract both columns and then add counter by GroupBy.cumcount to START_HOUR:
s = existing["END_HOUR"].sub(existing["START_HOUR"]) + 1
df = existing.loc[existing.index.repeat(s)].copy()
add = df.groupby(level=0).cumcount()
df['HOUR'] = df["START_HOUR"].add(add)
df = df.reset_index(drop=True).drop(['START_HOUR','END_HOUR'], axis=1)
The numpy busdays_count works but when I apply it to the dataframe I get errors because some of the dates are NaT (correctly).
If it was a normal array I could iterate each row, check if NaT and then apply the formulae but not sure here ...
data_raw['due'] = pd.to_datetime(data_raw['Due Date'], format="%Y%m%d")
data_raw['clo'] = pd.to_datetime(data_raw['Closed Date'], format="%Y%m%d")
data_raw['perf'] = data_raw.apply(lambda row: np.busday_count(row['due'].values.astype('datetime64[D]'),
row['clo'].values.astype('datetime64[D]')
if pd.isnull(row['clo'])
else '',
axis=1
))
The error is KeyError: 'due'
This works below but not sure on joining:
p_df = data_raw[pd.notna(data_raw.clo)]
p_df['perf'] = np.busday_count(p_df['due'].values.astype('datetime64[D]'), p_df['clo'].values.astype('datetime64[D]'))
I found a work around but pretty sure it is not the best way...
# split the dataframe
not_na = data_raw[pd.notna(data_raw.clo)]
is_na = data_raw[pd.isna(data_raw.clo)]
# do the calc without the NaNs
not_na['perf'] =
np.busday_count(not_na['due'].values.astype('datetime64[D]'),
not_na['clo'].values.astype('datetime64[D]'))
# lastly, join the dataframes back
new_df = pd.concat([is_na, not_na], axis=0)
I am trying concat several csv files by customer group using the below code:
files = glob.glob(file_from + "/*.csv") <<-- Path where the csv resides
df_v0 = pd.concat([pd.read_csv(f) for f in files]) <<-- Dataframe that concat all csv files from files mentioned above
The problem is the number of column in the csv varies by customer and they do not have a header file.
I am trying to see if I could add in a dummmy header column with labels such as col_1, col_2 ... depending on the number of columns in that csv.
Could anyone guide as to how could I get this done. Thanks.
Update on trying to search for a specific string in the Dataframe:
Sample Dataframe
col_1,col_2,col_3
fruit,grape,green
fruit,watermelon,red
fruit,orange,orange
fruit,apple,red
Trying to filter out rows having the word red and expect it to return rows 2 and 4.
Tried the below code:
df[~df.apply(lambda x: x.astype(str).str.contains('red')).any(axis=1)]
Use parameters header=None for default range columns 0, 1, 2 and skiprows=1 if necessary remove original columns names:
df_v0 = pd.concat([pd.read_csv(f, header=None, skiprows=1) for f in files])
If want also change columns names add rename:
dfs = [pd.read_csv(f, header=None, skiprows=1).rename(columns = lambda x: f'col_{x + 1}')
for f in files]
df_v0 = pd.concat(dfs)
We handle data from volunteers, that data is entered in to a form using ODK. When the data is downloaded the header (column names) row contains a lot of 'stuff' we don' t need. The pattern is as follows:
'Group1/most_common/G27'
I want to replace the column names (there can be up to 200) or create a copy of the DataFrame with column names that just contain the G-code (Gxxx). I think I got it.
What is the faster or better way to do this?
IS the output reliable in terms of sort order? As of now it appears that the results list is in the same order as the original list.
y = ['Group1/most common/G95', 'Group1/most common/G24', 'Group3/plastics/G132']
import re
r = []
for x in y:
m = re.findall(r'G\d+', x)
r.append(m)
# the comprehension below is to flatten it
# append.m gives me a list of lists (each list has one item)
results = [q for t in r for q in t]
print(results)
['G95', 'G24', 'G132']
The idea would be to iterate through the column names in the DataFrame (or a copy), delete what I don't need and replace (inplace=True).
Thanks for your input.
You can use str.extract:
df = pd.DataFrame(columns=['Group1/most common/G95',
'Group1/most common/G24',
'Group3/plastics/G132'])
print (df)
Empty DataFrame
Columns: [Group1/most common/G95, Group1/most common/G24, Group3/plastics/G132]
Index: []
df.columns = df.columns.str.extract('(G\d+)', expand=False)
print (df)
Empty DataFrame
Columns: [G95, G24, G132]
Index: []
Another solution with rsplit and select last values with [-1]:
df.columns = df.columns.str.rsplit('/').str[-1]
print (df)
Empty DataFrame
Columns: [G95, G24, G132]
Index: []