Python pandas setting df.iloc with conditional instead of fixed value

Question

I currently have a function where I am reading in over 25 .csv files. At the moment, I am taking the average over a fixed interval for the same starting and ending sample for each file (see in my code, below).

My goal is now to adjust the starting time to when Temp>50.
My pseudocode is like this:

Current: avg_Temp = df.iloc[63-1:115+1]["Temp"].mean()

Objective: avg_Temp = df.iloc[df[Temp>50]:115+1]["Temp"].mean()

What is the best way to accomplish this?

def plot_data(filename, fig_ax, color):
    
    df = pd.read_csv(f, sep=',',skiprows=24)    # Read in the csv.
    df.columns=['sample','Time','ms','Temp']    # Set the column names
    df=df.astype(str)                           # Set the data type as a string.

    df["Temp"] = df["Temp"].str.replace('\+ ', '').str.replace(' ', '').astype(float) # Convert to float
   
    # Take the average of the data from the Temp column, starting from sample 63 until sample 115.
    avg_Temp = df.iloc[63-1:115+1]["Temp"].mean()

Do you mean df.loc[df['temp']>50, 'Temp'].head(50).mean(0)? — Quang Hoang
– Quang Hoang, Commented Dec 13, 2020 at 19:30
Basically, I am trying to start the average when the Temp value is greater than 50; rather than setting a fixed starting value for each one. — Gary
– Gary, Commented Dec 13, 2020 at 19:38

Servus · Accepted Answer · 2020-12-13 19:39:03Z

1

Probably something like this

idx = (df['Temp'] > 50).idxmax()
avg_Temp = df.iloc[idx:115+1]["Temp"].mean()

answered Dec 13, 2020 at 19:39

Servus

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