Reputation: 422
I am trying to get calculate the mean for Score 1 only if column Dates
is equal to Oct-16
:
What I originally tried was:
import pandas as pd
import numpy as np
import os
dataFrame = pd.read_csv("test.csv")
for date in dataFrame["Dates"]:
if date == "Oct-16":
print(date)##Just checking
print(dataFrame["Score 1"].mean())
But my results are the mean for the whole column Score 1
Another thing I tried was manually telling it which indices to calculate the mean for:
dataFrame["Score 1"].iloc[0:2].mean()
But ideally I would like to find a way to do it if Dates == "Oct-16"
.
Upvotes: 4
Views: 10439
Reputation: 294508
How about the mean for all dates
dataframe.groupby('Dates').['Score 1'].mean()
Upvotes: 2
Reputation: 215
import pandas as pd
import numpy as np
import os
dataFrame = pd.read_csv("test.csv")
dates = dataFrame["Dates"]
score1s = dataFrame["Score 1"]
result = []
for i in range(0,len(dates)):
if dates[i] == "Oct-16":
result.append(score1s[i])
print(result.mean())
Upvotes: 1
Reputation: 11391
Iterating through the rows doesn't take advantage of Pandas' strengths. If you want to do something with a column based on values of another column, you can use .loc[]
:
dataFrame.loc[dataFrame['Dates'] == 'Oct-16', 'Score 1']
The first part of .loc[]
selects the rows you want, using your specified criteria (dataFrame['Dates'] == 'Oct-16'
). The second part specifies the column you want (Score 1
). Then if you want to get the mean, you can just put .mean()
on the end:
dataFrame.loc[dataFrame['Dates'] == 'Oct-16', 'Score 1'].mean()
Upvotes: 7