Learn how to filter Pandas dataframe using ‘in’ and ‘not in’ operators, similar to SQL. Master efficient data filtering techniques in Pandas for data analysis and manipulation.… Read More How to Filter Pandas Dataframe using ‘in’ and ‘not in’ like in SQL
Advertisements My DataFrame: Trying… Nope… Still nope… AND YET… Any idea why I can’t filter this dataframe by a list using isin? I’m expecting it to return one or more rows where the Chord Notes column equals the list [‘C’,’E’,’G’] >Solution : you can still use isin() but since the values under ‘Chord Notes’ are… Read More .isin() returning a blank dataframe
Advertisements I have a dropdown menu: dcc.Dropdown( id="select", options = list(all_df[‘Device’].unique()), value = list(all_df[‘Device’].unique()[0]) ) dcc.Graph(id = ‘shared’ , figure={} ), to enable a selection of a device (or devices) to plot later on: @app.callback( Output("shared", "figure"), Input("select", "value") ) def update_signal_chart(select): df4 = all_df[all_df[‘Device’].isin(select)] (…) and whatever I try, it’s always resulting in TypeError:… Read More Only list-like objects are allowed to be passed to isn()
Advertisements I have a problem, I try many things and I can’t manage to do this. It’s a code that check if the delivery numbers are on our sales report. Here it’s the code: dfn = dfn.astype(‘str’) diff = dfn[~dfn[‘Numero de Envio’].isin(dfn[‘Unnamed: 13’])].dropna()[[‘Numero de Envio’,’Fecha’]].values.tolist() print(f"\n Hay ventas que podrían no ser nuestras: \U0001F611 -… Read More How to split a tolist result in two groups (new lists)?
Advertisements I have a benchmark df_1: Col_1 insight_id Col_2 Col_n 24249 ABC123 656 AAA 24249 ABC123 670 AXA 22549 ABC124 656 AAC 24249 ABC124 656 ADA 24236 ABC125 656 AAA And a dataset df_2: Col_a insight_id Col_b Col_x 24299 ABC123 956 XAA 24299 ABC123 970 AXX 24299 ABC125 954 AAX 24299 ABC125 956 AXX How… Read More Flag column values that are not present in another dataframe
Advertisements Evening… revising some C# coding questions/// 10 Random numbers 7319018271 ….so I put brackets around the numbers and made it a string… created an array with [10]…idea is to iterate through the string and convert each string var to an int and add to myArray…problem being…why does 7 return as 55? Thanks var Numbers… Read More C# -> 7 in a string, convert to int returns 55?
Advertisements I am trying to store coefficients from a mle model just like a normally do for other models. Surprisingly, it throws an error. Any other methods to store it? set.seed(111) y.size <- rnorm(100,4,1) p <- rnorm(100,5,1) df <- data.frame(p, y.size) glae <- function(A,K,Ka,q, sigma, c) { lnqp <- if(q == 0) log(p) else ((p^q… Read More How to get coefficients from a model in R?
Advertisements I have a list nested dictionary that looks in the following way and I have an epoch timestamp stored as a variable. ts = 1653070640 [ { "HR": { "EKG": { "HR_EKG": 136.0 }, "PPG": { "HR_PPG_1": 135.0, "HR_PULSED": 135.0 } }, "NIBP": { "DBP": { "NIBPD": 25.0 }, "MBP": { "NIBPM": 53.0 },… Read More Adding a new element to a nested dictionary in python
Advertisements I’ve got the following code, but unfortunately it only changes the value inside the list. Is there any way I can change the value outside the list, so it can be used later in the script? street_number = "100 & 102" street_name = "Fake Street" suburb = "Faketown" allvariables = [street_number, street_name, suburb] ampersand… Read More Need to check all my variables for an ampersand, I put variables in a list and check using a for loop, but Python only changes value inside list
Advertisements I have dataframe with column d1 and now i am trying calculate ‘out’ column after ranking that column when there in ‘nan’ value with in a column. data_input = {‘Name’:[‘Renault’, ‘Renault’, ‘Renault’, ‘Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’], ‘type’:[‘Duster’, ‘Duster’, ‘Duster’,’Duster’,’Duster’,’Duster’,’Duster’,’Triber’,’Triber’,’Triber’,’Triber’,’Triber’,’Triber’,’Triber’], ‘d1’:[‘nan’,’10’,’10’,’10’,’nan’,’nan’,’20’,’20’,’nan’,’nan’,’30’,’30’,’30’,’nan’]} df_input = pd.DataFrame(data_input) data_out = {‘Name’:[‘Renault’, ‘Renault’, ‘Renault’, ‘Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’], ‘type’:[‘Duster’, ‘Duster’, ‘Duster’,’Duster’,’Duster’,’Duster’,’Duster’,’Triber’,’Triber’,’Triber’,’Triber’,’Triber’,’Triber’,’Triber’], ‘d1’:[‘nan’,’10’,’10’,’10’,’nan’,’nan’,’20’,’20’,’nan’,’nan’,’30’,’30’,’30’,’nan’], ‘out’:[1,np.NaN,np.NaN,np.NaN,2,2,np.NaN,np.NaN,1,1,np.NaN,np.NaN,np.NaN,2]} df_out = pd.DataFrame(data_out)… Read More rank for nan values based on group
