Mixing
Performing task for a particular datatype alone in python
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I have a dataframe like the one below:
Here's the code to build this dataframe:
df = pd.DataFrame({'Id': ['A12', 'B18', 'C78'],
'Age': [55, 45, 58],
'Hobbies': ['Movies', 'Walking', 'Travelling'],
'Salary': [60000, 55000, 85000]})
I need to pass the entire dataframe in a loop where is perform the mean function for the integer data type alone (in my example its age and salary) leaving the rest of datatype as it is. Could anyone help me in solving this problem?
python python-3.x pandas
edited Jan 3 at 13:39
jpp
103k2167117
asked Jan 3 at 10:29
YadhuYadhu
658
add a comment |
I have a dataframe like the one below:
Here's the code to build this dataframe:
df = pd.DataFrame({'Id': ['A12', 'B18', 'C78'],
'Age': [55, 45, 58],
'Hobbies': ['Movies', 'Walking', 'Travelling'],
'Salary': [60000, 55000, 85000]})
I need to pass the entire dataframe in a loop where is perform the mean function for the integer data type alone (in my example its age and salary) leaving the rest of datatype as it is. Could anyone help me in solving this problem?
python python-3.x pandas
edited Jan 3 at 13:39
jpp
103k2167117
asked Jan 3 at 10:29
YadhuYadhu
658
As shown in the image, if you know the columns names, I guess you can explicitly define the columns on which mean needs to be performed.
– Sam
Jan 3 at 10:38
This is just an example in my real time data I have more than 250+ column names bro.
– Yadhu
Jan 3 at 10:40
In that case , you can add a condition to your function to check for datatype of column and perform mean only of it matches Integer datatype. for e.gif type(column[0])==type(1):perform mean.
– Sam
Jan 3 at 10:46
add a comment |
I have a dataframe like the one below:
Here's the code to build this dataframe:
df = pd.DataFrame({'Id': ['A12', 'B18', 'C78'],
'Age': [55, 45, 58],
'Hobbies': ['Movies', 'Walking', 'Travelling'],
'Salary': [60000, 55000, 85000]})
I need to pass the entire dataframe in a loop where is perform the mean function for the integer data type alone (in my example its age and salary) leaving the rest of datatype as it is. Could anyone help me in solving this problem?
python python-3.x pandas
edited Jan 3 at 13:39
jpp
103k2167117
asked Jan 3 at 10:29
YadhuYadhu
658
I have a dataframe like the one below:
Here's the code to build this dataframe:
df = pd.DataFrame({'Id': ['A12', 'B18', 'C78'],
'Age': [55, 45, 58],
'Hobbies': ['Movies', 'Walking', 'Travelling'],
'Salary': [60000, 55000, 85000]})
I need to pass the entire dataframe in a loop where is perform the mean function for the integer data type alone (in my example its age and salary) leaving the rest of datatype as it is. Could anyone help me in solving this problem?
python python-3.x pandas
python python-3.x pandas
edited Jan 3 at 13:39
jpp
103k2167117
asked Jan 3 at 10:29
YadhuYadhu
658
edited Jan 3 at 13:39
jpp
103k2167117
asked Jan 3 at 10:29
YadhuYadhu
658
edited Jan 3 at 13:39
jpp
103k2167117
edited Jan 3 at 13:39
jpp
103k2167117
edited Jan 3 at 13:39
jpp
103k2167117
103k2167117
asked Jan 3 at 10:29
YadhuYadhu
658
asked Jan 3 at 10:29
YadhuYadhu
658
asked Jan 3 at 10:29
YadhuYadhu
658
658
As shown in the image, if you know the columns names, I guess you can explicitly define the columns on which mean needs to be performed.
– Sam
Jan 3 at 10:38
This is just an example in my real time data I have more than 250+ column names bro.
– Yadhu
Jan 3 at 10:40
In that case , you can add a condition to your function to check for datatype of column and perform mean only of it matches Integer datatype. for e.gif type(column[0])==type(1):perform mean.
– Sam
Jan 3 at 10:46
add a comment |
As shown in the image, if you know the columns names, I guess you can explicitly define the columns on which mean needs to be performed.
– Sam
Jan 3 at 10:38
This is just an example in my real time data I have more than 250+ column names bro.
– Yadhu
Jan 3 at 10:40
In that case , you can add a condition to your function to check for datatype of column and perform mean only of it matches Integer datatype. for e.gif type(column[0])==type(1):perform mean.
– Sam
Jan 3 at 10:46
As shown in the image, if you know the columns names, I guess you can explicitly define the columns on which mean needs to be performed.
– Sam
Jan 3 at 10:38
As shown in the image, if you know the columns names, I guess you can explicitly define the columns on which mean needs to be performed.
– Sam
Jan 3 at 10:38
This is just an example in my real time data I have more than 250+ column names bro.
– Yadhu
Jan 3 at 10:40
This is just an example in my real time data I have more than 250+ column names bro.
– Yadhu
Jan 3 at 10:40
In that case , you can add a condition to your function to check for datatype of column and perform mean only of it matches Integer datatype. for e.g
if type(column[0])==type(1): perform mean.– Sam
Jan 3 at 10:46
In that case , you can add a condition to your function to check for datatype of column and perform mean only of it matches Integer datatype. for e.g
if type(column[0])==type(1): perform mean.– Sam
Jan 3 at 10:46
add a comment |
1 Answer
1
active
oldest
votes
select_dtypes + mean
Select numeric series and then calculate the mean:
res = df.select_dtypes(include=['number']).mean()
print(res)
# Age 52.666667
# Salary 66666.666667
# dtype: float64
To strictly include only int series, so that float series are excluded, you can use:
res = df.select_dtypes(include=['int']).mean()
answered Jan 3 at 13:39
jppjpp
103k2167117
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
select_dtypes + mean
Select numeric series and then calculate the mean:
res = df.select_dtypes(include=['number']).mean()
print(res)
# Age 52.666667
# Salary 66666.666667
# dtype: float64
To strictly include only int series, so that float series are excluded, you can use:
res = df.select_dtypes(include=['int']).mean()
answered Jan 3 at 13:39
jppjpp
103k2167117
add a comment |
select_dtypes + mean
Select numeric series and then calculate the mean:
res = df.select_dtypes(include=['number']).mean()
print(res)
# Age 52.666667
# Salary 66666.666667
# dtype: float64
To strictly include only int series, so that float series are excluded, you can use:
res = df.select_dtypes(include=['int']).mean()
answered Jan 3 at 13:39
jppjpp
103k2167117
add a comment |
select_dtypes + mean
Select numeric series and then calculate the mean:
res = df.select_dtypes(include=['number']).mean()
print(res)
# Age 52.666667
# Salary 66666.666667
# dtype: float64
To strictly include only int series, so that float series are excluded, you can use:
res = df.select_dtypes(include=['int']).mean()
answered Jan 3 at 13:39
jppjpp
103k2167117
select_dtypes + mean
Select numeric series and then calculate the mean:
res = df.select_dtypes(include=['number']).mean()
print(res)
# Age 52.666667
# Salary 66666.666667
# dtype: float64
To strictly include only int series, so that float series are excluded, you can use:
res = df.select_dtypes(include=['int']).mean()
answered Jan 3 at 13:39
jppjpp
103k2167117
answered Jan 3 at 13:39
jppjpp
103k2167117
answered Jan 3 at 13:39
jppjpp
103k2167117
answered Jan 3 at 13:39
jppjpp
103k2167117
103k2167117
add a comment |
add a comment |
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As shown in the image, if you know the columns names, I guess you can explicitly define the columns on which mean needs to be performed.
– Sam
Jan 3 at 10:38
This is just an example in my real time data I have more than 250+ column names bro.
– Yadhu
Jan 3 at 10:40
In that case , you can add a condition to your function to check for datatype of column and perform mean only of it matches Integer datatype. for e.g
if type(column[0])==type(1):perform mean.– Sam
Jan 3 at 10:46