Parse CSV with far future dates to Parquet - pandas

I’m trying to read a CSV into Pandas, and then write it to Parquet. The challenge is that the CSV has a date column with a value of 3000-12-31, and apparently Pandas has no way to store that value as an actual date. Because of that, PyArrow fails to read the date value.
An example file and code to reproduce is
test.csv
t
3000-12-31
import pandas as pd
import pyarrow as pa
df = pd.read_csv("test.csv", parse_dates=["t"])
schema = pa.schema([pa.field("t", pa.date64())])
table = pa.Table.from_pandas(df, schema=schema)
This gives (a somewhat unhelpful error)
TypeError: an integer is required (got type str)
What's the right way to do this?


Pandas datetime columns (which use the datetime64[ns] data type) indeed cannot store such dates.
One possible workaround to convert the strings to datetime.datetime objects in an object dtype column. And then pyarrow should be able to accept them to create a date column.
This conversion could eg be done with dateutil:
>>> import dateutil
>>> df['t'] = df['t'].apply(dateutil.parser.parse)
>>> df
t
0 3000-12-31 00:00:00
>>> table = pa.Table.from_pandas(df, schema=schema)
>>> table
pyarrow.Table
t: date64[ms]
or if you use a fixed format, using datetime.date.strptime is probably more reliable:
>>> import datetime
>>> df['t'] = df['t'].apply(lambda s: datetime.datetime.strptime(s, "%Y-%m-%d"))
>>> table = pa.Table.from_pandas(df, schema=schema)
>>> table
pyarrow.Table
t: date64[ms]

Related

dtype definition for pandas dataframe with columns of VARCHAR or String

I want to get some data in a dictionary that need to go into a pandas dataframe.
The dataframe is later written in a PostgreSQL table using sqlalchemy, and I would like to get the right column types.
Hence, I specify the dtypes for the dataframe
dtypes = {"forretningshændelse": sqlalchemy.types.String(length=8),
"forretningsområde": sqlalchemy.types.String(length=40),
"forretningsproces": sqlalchemy.types.INTEGER(),
"id_namespace": sqlalchemy.types.String(length=100),
"id_lokalId": sqlalchemy.types.String(length=36),
"kommunekode": sqlalchemy.types.INTEGER(),
"registreringFra": sqlalchemy.types.DateTime()}
Later I use df = pd.DataFrame(item_lst, dtype=dtypes), where item_lst is a list of dictionaries.
Independent from me using either String(8), String(length=8) or VARCHAR(8) in the dtype definition, the result of pd.DataFrame(item_lst, dtype=dtypes) is always object of type '(String or VARCHAR)' has no len().
How do I have to define the dtype to overcome this error?

Instead of forcing data types when the DataFrame is created, let pandas infer the data types (just df = pd.DataFrame(item_lst)) and then use your dtypes dict with to_sql() when you push your DataFrame to the database, like this:
from pprint import pprint
import pandas as pd
import sqlalchemy
engine = sqlalchemy.create_engine("sqlite://")
item_lst = [{"forretningshændelse": "foo"}]
df = pd.DataFrame(item_lst)
print(df.info())
"""
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1 entries, 0 to 0
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 forretningshændelse 1 non-null object
dtypes: object(1)
memory usage: 136.0+ bytes
None
"""
dtypes = {"forretningshændelse": sqlalchemy.types.String(length=8)}
df.to_sql("tbl", engine, index=False, dtype=dtypes)
insp = sqlalchemy.inspect(engine)
pprint(insp.get_columns("tbl"))
"""
[{'autoincrement': 'auto',
'default': None,
'name': 'forretningshændelse',
'nullable': True,
'primary_key': 0,
'type': VARCHAR(length=8)}]
"""

I believe you are confusing the dtypes within the DataFrame with the dtypes on the SQL table itself.
You probably don't need to manually specify the datatypes in pandas itself but if you do, here's how.
Spoiler alert: it is written in the pandas.Dataframe documentation that only a single dtype must be specified so you will need some loops or manual column work to get different types.
To solve your problem:
import pandas as pd
import sqlalchemy
engine = sqlalchemy.create_engine("connection_string")
df = pd.DataFrame(item_list)
dtypes = {"forretningshændelse": sqlalchemy.types.String(length=8),
"forretningsområde": sqlalchemy.types.String(40),
"forretningsproces": sqlalchemy.types.INTEGER(),
"id_namespace": sqlalchemy.types.String(100),
"id_lokalId": sqlalchemy.types.String(36),
"kommunekode": sqlalchemy.types.INTEGER(),
"registreringFra": sqlalchemy.types.DateTime()}
with engine.connect() as engine:
df.to_sql("table_name",if_exists="replace", con=engine, dtype=dtypes)
Tip: Avoid using special characters while coding in general, it only makes maintaining code harder at some point :). I assumed you're creating a new sql table and not appending, otherwise types for the table would already be defined.
Happy Coding!

Python Pandas : Read dates from excel in different formats [duplicate]

I have one field in a pandas DataFrame that was imported as string format.
It should be a datetime variable. How do I convert it to a datetime column and then filter based on date.
Example:
df = pd.DataFrame({'date': ['05SEP2014:00:00:00.000']})

Use the to_datetime function, specifying a format to match your data.
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], format='%d%b%Y:%H:%M:%S.%f')

If you have more than one column to be converted you can do the following:
df[["col1", "col2", "col3"]] = df[["col1", "col2", "col3"]].apply(pd.to_datetime)

You can use the DataFrame method .apply() to operate on the values in Mycol:
>>> df = pd.DataFrame(['05SEP2014:00:00:00.000'],columns=['Mycol'])
>>> df
Mycol
0 05SEP2014:00:00:00.000
>>> import datetime as dt
>>> df['Mycol'] = df['Mycol'].apply(lambda x:
dt.datetime.strptime(x,'%d%b%Y:%H:%M:%S.%f'))
>>> df
Mycol
0 2014-09-05

Use the pandas to_datetime function to parse the column as DateTime. Also, by using infer_datetime_format=True, it will automatically detect the format and convert the mentioned column to DateTime.
import pandas as pd
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], infer_datetime_format=True)

chrisb's answer works:
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], format='%d%b%Y:%H:%M:%S.%f')
however it results in a Python warning of
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
I would guess this is due to some chaining indexing.

Time Saver:
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'])

To silence SettingWithCopyWarning
If you got this warning, then that means your dataframe was probably created by filtering another dataframe. Make a copy of your dataframe before any assignment and you're good to go.
df = df.copy()
df['date'] = pd.to_datetime(df['date'], format='%d%b%Y:%H:%M:%S.%f')
errors='coerce' is useful
If some rows are not in the correct format or not datetime at all, errors= parameter is very useful, so that you can convert the valid rows and handle the rows that contained invalid values later.
df['date'] = pd.to_datetime(df['date'], format='%d%b%Y:%H:%M:%S.%f', errors='coerce')
# for multiple columns
df[['start', 'end']] = df[['start', 'end']].apply(pd.to_datetime, format='%d%b%Y:%H:%M:%S.%f', errors='coerce')
Setting the correct format= is much faster than letting pandas find out1
Long story short, passing the correct format= from the beginning as in chrisb's post is much faster than letting pandas figure out the format, especially if the format contains time component. The runtime difference for dataframes greater than 10k rows is huge (~25 times faster, so we're talking like a couple minutes vs a few seconds). All valid format options can be found at https://strftime.org/.
1 Code used to produce the timeit test plot.
import perfplot
from random import choices
from datetime import datetime
mdYHMSf = range(1,13), range(1,29), range(2000,2024), range(24), *[range(60)]*2, range(1000)
perfplot.show(
kernels=[lambda x: pd.to_datetime(x),
lambda x: pd.to_datetime(x, format='%m/%d/%Y %H:%M:%S.%f'),
lambda x: pd.to_datetime(x, infer_datetime_format=True),
lambda s: s.apply(lambda x: datetime.strptime(x, '%m/%d/%Y %H:%M:%S.%f'))],
labels=["pd.to_datetime(df['date'])",
"pd.to_datetime(df['date'], format='%m/%d/%Y %H:%M:%S.%f')",
"pd.to_datetime(df['date'], infer_datetime_format=True)",
"df['date'].apply(lambda x: datetime.strptime(x, '%m/%d/%Y %H:%M:%S.%f'))"],
n_range=[2**k for k in range(20)],
setup=lambda n: pd.Series([f"{m}/{d}/{Y} {H}:{M}:{S}.{f}"
for m,d,Y,H,M,S,f in zip(*[choices(e, k=n) for e in mdYHMSf])]),
equality_check=pd.Series.equals,
xlabel='len(df)'
)

Just like we convert object data type to float or int. Use astype()
raw_data['Mycol']=raw_data['Mycol'].astype('datetime64[ns]')

How to add "-" inside string values in Pandas [duplicate]

I have one field in a pandas DataFrame that was imported as string format.
It should be a datetime variable. How do I convert it to a datetime column and then filter based on date.
Example:
df = pd.DataFrame({'date': ['05SEP2014:00:00:00.000']})

Use the to_datetime function, specifying a format to match your data.
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], format='%d%b%Y:%H:%M:%S.%f')

If you have more than one column to be converted you can do the following:
df[["col1", "col2", "col3"]] = df[["col1", "col2", "col3"]].apply(pd.to_datetime)

You can use the DataFrame method .apply() to operate on the values in Mycol:
>>> df = pd.DataFrame(['05SEP2014:00:00:00.000'],columns=['Mycol'])
>>> df
Mycol
0 05SEP2014:00:00:00.000
>>> import datetime as dt
>>> df['Mycol'] = df['Mycol'].apply(lambda x:
dt.datetime.strptime(x,'%d%b%Y:%H:%M:%S.%f'))
>>> df
Mycol
0 2014-09-05

Use the pandas to_datetime function to parse the column as DateTime. Also, by using infer_datetime_format=True, it will automatically detect the format and convert the mentioned column to DateTime.
import pandas as pd
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], infer_datetime_format=True)

chrisb's answer works:
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], format='%d%b%Y:%H:%M:%S.%f')
however it results in a Python warning of
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
I would guess this is due to some chaining indexing.

Time Saver:
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'])

To silence SettingWithCopyWarning
If you got this warning, then that means your dataframe was probably created by filtering another dataframe. Make a copy of your dataframe before any assignment and you're good to go.
df = df.copy()
df['date'] = pd.to_datetime(df['date'], format='%d%b%Y:%H:%M:%S.%f')
errors='coerce' is useful
If some rows are not in the correct format or not datetime at all, errors= parameter is very useful, so that you can convert the valid rows and handle the rows that contained invalid values later.
df['date'] = pd.to_datetime(df['date'], format='%d%b%Y:%H:%M:%S.%f', errors='coerce')
# for multiple columns
df[['start', 'end']] = df[['start', 'end']].apply(pd.to_datetime, format='%d%b%Y:%H:%M:%S.%f', errors='coerce')
Setting the correct format= is much faster than letting pandas find out1
Long story short, passing the correct format= from the beginning as in chrisb's post is much faster than letting pandas figure out the format, especially if the format contains time component. The runtime difference for dataframes greater than 10k rows is huge (~25 times faster, so we're talking like a couple minutes vs a few seconds). All valid format options can be found at https://strftime.org/.
1 Code used to produce the timeit test plot.
import perfplot
from random import choices
from datetime import datetime
mdYHMSf = range(1,13), range(1,29), range(2000,2024), range(24), *[range(60)]*2, range(1000)
perfplot.show(
kernels=[lambda x: pd.to_datetime(x),
lambda x: pd.to_datetime(x, format='%m/%d/%Y %H:%M:%S.%f'),
lambda x: pd.to_datetime(x, infer_datetime_format=True),
lambda s: s.apply(lambda x: datetime.strptime(x, '%m/%d/%Y %H:%M:%S.%f'))],
labels=["pd.to_datetime(df['date'])",
"pd.to_datetime(df['date'], format='%m/%d/%Y %H:%M:%S.%f')",
"pd.to_datetime(df['date'], infer_datetime_format=True)",
"df['date'].apply(lambda x: datetime.strptime(x, '%m/%d/%Y %H:%M:%S.%f'))"],
n_range=[2**k for k in range(20)],
setup=lambda n: pd.Series([f"{m}/{d}/{Y} {H}:{M}:{S}.{f}"
for m,d,Y,H,M,S,f in zip(*[choices(e, k=n) for e in mdYHMSf])]),
equality_check=pd.Series.equals,
xlabel='len(df)'
)

Just like we convert object data type to float or int. Use astype()
raw_data['Mycol']=raw_data['Mycol'].astype('datetime64[ns]')

Converting DataFrame into sql

I am using the following code to convert my pandas into sql, but I get the following error although my dtype is float64 for this particular column.
I have tried to convert my dtype to str, but this did not work.
import sqlite3
import pandas as pd
#create db file
db = conn = sqlite3.connect(‘example.db’)
#convert my df data to sql
df = df(‘users’ , con=db, if_exists='replace')
InterfaceError: Error binding parameter 1214 - probably unsupported type.
However when I check the parameter 1214 i.e. column 1214 in my df. This col has a float64 dtype. I don't understand then how to solve this problem.

Double check your data types, as SQLite supports a limited number of data types --> https://www.sqlite.org/datatype3.html. My guess would be to use a float dtype (so try dtype='float')

Pandas date comparison giving Invalid type comparison error [duplicate]

I have one field in a pandas DataFrame that was imported as string format.
It should be a datetime variable. How do I convert it to a datetime column and then filter based on date.
Example:
df = pd.DataFrame({'date': ['05SEP2014:00:00:00.000']})

Use the to_datetime function, specifying a format to match your data.
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], format='%d%b%Y:%H:%M:%S.%f')

If you have more than one column to be converted you can do the following:
df[["col1", "col2", "col3"]] = df[["col1", "col2", "col3"]].apply(pd.to_datetime)

You can use the DataFrame method .apply() to operate on the values in Mycol:
>>> df = pd.DataFrame(['05SEP2014:00:00:00.000'],columns=['Mycol'])
>>> df
Mycol
0 05SEP2014:00:00:00.000
>>> import datetime as dt
>>> df['Mycol'] = df['Mycol'].apply(lambda x:
dt.datetime.strptime(x,'%d%b%Y:%H:%M:%S.%f'))
>>> df
Mycol
0 2014-09-05

Use the pandas to_datetime function to parse the column as DateTime. Also, by using infer_datetime_format=True, it will automatically detect the format and convert the mentioned column to DateTime.
import pandas as pd
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], infer_datetime_format=True)

chrisb's answer works:
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], format='%d%b%Y:%H:%M:%S.%f')
however it results in a Python warning of
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
I would guess this is due to some chaining indexing.

Time Saver:
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'])

To silence SettingWithCopyWarning
If you got this warning, then that means your dataframe was probably created by filtering another dataframe. Make a copy of your dataframe before any assignment and you're good to go.
df = df.copy()
df['date'] = pd.to_datetime(df['date'], format='%d%b%Y:%H:%M:%S.%f')
errors='coerce' is useful
If some rows are not in the correct format or not datetime at all, errors= parameter is very useful, so that you can convert the valid rows and handle the rows that contained invalid values later.
df['date'] = pd.to_datetime(df['date'], format='%d%b%Y:%H:%M:%S.%f', errors='coerce')
# for multiple columns
df[['start', 'end']] = df[['start', 'end']].apply(pd.to_datetime, format='%d%b%Y:%H:%M:%S.%f', errors='coerce')
Setting the correct format= is much faster than letting pandas find out1
Long story short, passing the correct format= from the beginning as in chrisb's post is much faster than letting pandas figure out the format, especially if the format contains time component. The runtime difference for dataframes greater than 10k rows is huge (~25 times faster, so we're talking like a couple minutes vs a few seconds). All valid format options can be found at https://strftime.org/.
1 Code used to produce the timeit test plot.
import perfplot
from random import choices
from datetime import datetime
mdYHMSf = range(1,13), range(1,29), range(2000,2024), range(24), *[range(60)]*2, range(1000)
perfplot.show(
kernels=[lambda x: pd.to_datetime(x),
lambda x: pd.to_datetime(x, format='%m/%d/%Y %H:%M:%S.%f'),
lambda x: pd.to_datetime(x, infer_datetime_format=True),
lambda s: s.apply(lambda x: datetime.strptime(x, '%m/%d/%Y %H:%M:%S.%f'))],
labels=["pd.to_datetime(df['date'])",
"pd.to_datetime(df['date'], format='%m/%d/%Y %H:%M:%S.%f')",
"pd.to_datetime(df['date'], infer_datetime_format=True)",
"df['date'].apply(lambda x: datetime.strptime(x, '%m/%d/%Y %H:%M:%S.%f'))"],
n_range=[2**k for k in range(20)],
setup=lambda n: pd.Series([f"{m}/{d}/{Y} {H}:{M}:{S}.{f}"
for m,d,Y,H,M,S,f in zip(*[choices(e, k=n) for e in mdYHMSf])]),
equality_check=pd.Series.equals,
xlabel='len(df)'
)

Just like we convert object data type to float or int. Use astype()
raw_data['Mycol']=raw_data['Mycol'].astype('datetime64[ns]')