Is this a known issue that specifying CategoricalDtype dtype at read_json does not convert the column dtype, or is there a mistake in the code? import pandas as pd df = pd.read_json( "./data/data.json", dtype={ #"facility": pd.CategoricalDtype, # does not work "facility": ‘category’, # does work "supplier": pd.CategoricalDtype, # does not work } ) df.info() —–… Read More pandas read_json dtype=pd.CategoricalDtype does not work but dtype='category' does

Since they are in object format, I am trying to create a new variable by converting prices in my df to a numeric value. I tried to remove the ‘,’ and ‘$’ from the numbers in the column and then convert them to a different type with pd.to_numeric df_l[‘price_MXN2’] = df_l[‘price_MXN’].str.replace(‘,’,”) df_l[‘price_MXN2’] = df_l[‘price_MXN’].str.replace(‘$’,”) df_l[‘price_MXN2’]… Read More Converting currency to numeric value

I’ve encountered this strange behavior of pandas .astype() (I’m using version 1.5.2). When trying to cast a column as integer, and later requesting dtypes, all seems fine. Until you try to extract the values by row, when you get inconsistent types. Code: import pandas as pd import numpy as np ​ df = pd.DataFrame(np.random.randn(3, 3))… Read More pandas astype doesn't work as expected (fails silently and badly)

I am learning about dtypes in numpy and I have the following doubt. I can define a compound type as follows: myrecord = np.dtype([ (‘col1’, ‘u4’), (‘col2’, ‘f8’) ]) If I have two individual numpy arrays: a=np.array([1,2,3,4]) b=np.array([10.1,20.1,30.1,40.1]) How would I generate a third array c of type my_record? This is what I tried, which… Read More How do I create a compound dtype numpy array from existing individual vectors?