Try doing this:
week_grouped = df.groupby('week')
week_grouped.sum().reset_index().to_csv('week_grouped.csv')
That'll write the entire dataframe to the file. If you only want those two columns then,
week_grouped = df.groupby('week')
week_grouped.sum().reset_index()[['week', 'count']].to_csv('week_grouped.csv')
Here's a line by line explanation of the original code:
# This creates a "groupby" object (not a dataframe object)
# and you store it in the week_grouped variable.
week_grouped = df.groupby('week')
# This instructs pandas to sum up all the numeric type columns in each
# group. This returns a dataframe where each row is the sum of the
# group's numeric columns. You're not storing this dataframe in your
# example.
week_grouped.sum()
# Here you're calling the to_csv method on a groupby object... but
# that object type doesn't have that method. Dataframes have that method.
# So we should store the previous line's result (a dataframe) into a variable
# and then call its to_csv method.
week_grouped.to_csv('week_grouped.csv')
# Like this:
summed_weeks = week_grouped.sum()
summed_weeks.to_csv('...')
# Or with less typing simply
week_grouped.sum().to_csv('...')
Try doing this:
week_grouped = df.groupby('week')
week_grouped.sum().reset_index().to_csv('week_grouped.csv')
That'll write the entire dataframe to the file. If you only want those two columns then,
week_grouped = df.groupby('week')
week_grouped.sum().reset_index()[['week', 'count']].to_csv('week_grouped.csv')
Here's a line by line explanation of the original code:
# This creates a "groupby" object (not a dataframe object)
# and you store it in the week_grouped variable.
week_grouped = df.groupby('week')
# This instructs pandas to sum up all the numeric type columns in each
# group. This returns a dataframe where each row is the sum of the
# group's numeric columns. You're not storing this dataframe in your
# example.
week_grouped.sum()
# Here you're calling the to_csv method on a groupby object... but
# that object type doesn't have that method. Dataframes have that method.
# So we should store the previous line's result (a dataframe) into a variable
# and then call its to_csv method.
week_grouped.to_csv('week_grouped.csv')
# Like this:
summed_weeks = week_grouped.sum()
summed_weeks.to_csv('...')
# Or with less typing simply
week_grouped.sum().to_csv('...')
Group By returns key, value pairs where key is the identifier of the group and the value is the group itself, i.e. a subset of an original df that matched the key.
In your example week_grouped = df.groupby('week') is set of groups (pandas.core.groupby.DataFrameGroupBy object) which you can explore in detail as follows:
for k, gr in week_grouped:
# do your stuff instead of print
print(k)
print(type(gr)) # This will output <class 'pandas.core.frame.DataFrame'>
print(gr)
# You can save each 'gr' in a csv as follows
gr.to_csv('{}.csv'.format(k))
Or alternatively you can compute aggregation function on your grouped object
result = week_grouped.sum()
# This will be already one row per key and its aggregation result
result.to_csv('result.csv')
In your example you need to assign the function result to some variable as by default pandas objects are immutable.
some_variable = week_grouped.sum()
some_variable.to_csv('week_grouped.csv') # This will work
basically result.csv and week_grouped.csv are meant to be same
Hello,
Has anyone ever come across this before?
I'm trying to group some data in a dataframe and getting this error. The steps I've taken are:
-
in a for loop:
read in a csv from an api using pd.read_csv() replaced some values in a column using a for loop and .loc[] appended the resulting data frame to a list
2) concatenated the list of dataframes using pd.concat()
3) added a calculated column to the new DF by multiplying another column
4) added two empty columns
5) filtered the DF using .loc[] based on a value within a column
6) filtered the DF using .loc[] based on a value in a different column
7) tried to use this code:
new_DF = old_df.group_by(['col1', 'col_2', 'col_3', 'adgroup', 'col_4', 'col5', 'col6'], as_index=False)[['col7', 'col8', 'col9']].sum()
The DF seems to behaving normally for example I can do dtypes and columns on it and add columns which are calculated from other columns. What is super frustrating is that I can do pd.to_csv() and then pd.read_csv() on the DF and then I'm able to do the grouping I want (however this isn't ideal which is why I'm posting).
Any advice would be appreciated.
Cheers
pandas.core.groupby.DataFrameGroupBy to_csv method doesn't ouput csv file as expected
[Enhancement] Bumping pandas version from 1.5.0 to 2.x
python - Error 'AttributeError: 'DataFrameGroupBy' object has no attribute' while groupby functionality on dataframe - Stack Overflow
df_high = df_all[['model_cd', 'modelname', 'installcost',
'yearlyupkeep', 'Efficiency']][df_all['Efficiency']=="High"]
high_efficiency_model = df_high[['model_cd', 'modelname',
'installcost', 'yearlyupkeep',
'Efficiency']].groupby('Efficiency')
high_efficiency_model.to_csv(index = True)
keep getting an error saying :
AttributeError: 'DataFrameGroupBy'
The function pd.read_csv() is already a DataFrame and thus that kind of object does not support calling .to_dataframe().
You can check the type of your variable ds using print(type(ds)), you will see that it is a pandas DataFrame type.
According to what I understand. You are loading loanapp_c.csv in ds using this code:
ds = pd.read_csv('desktop/python ML/loanapp_c.csv')
ds over here is a DataFrame object. What you are doing is calling to_dataframe on an object which a DataFrame already.
Removing this dataset = ds.to_dataframe() from your code should solve the error
"sklearn.datasets" is a scikit package, where it contains a method load_iris().
load_iris(), by default return an object which holds data, target and other members in it. In order to get actual values you have to read the data and target content itself.
Whereas 'iris.csv', holds feature and target together.
FYI: If you set return_X_y as True in load_iris(), then you will directly get features and target.
from sklearn import datasets
data,target = datasets.load_iris(return_X_y=True)
The Iris Dataset from Sklearn is in Sklearn's Bunch format:
print(type(iris))
print(iris.keys())
output:
<class 'sklearn.utils.Bunch'>
dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])
So, that's why you can access it as:
x=iris.data
y=iris.target
But when you read the CSV file as DataFrame as mentioned by you:
iris = pd.read_csv('iris.csv',header=None).iloc[:,2:4]
iris.head()
output is:
2 3
0 petal_length petal_width
1 1.4 0.2
2 1.4 0.2
3 1.3 0.2
4 1.5 0.2
Here the column names are '1' and '2'.
First of all you should read the CSV file as:
df = pd.read_csv('iris.csv')
you should not include header=None as your csv file includes the column names i.e. the headers.
So, now what you can do is something like this:
X = df.iloc[:, [2, 3]] # Will give you columns 2 and 3 i.e 'petal_length' and 'petal_width'
y = df.iloc[:, 4] # Label column i.e 'species'
or if you want to use the column names then:
X = df[['petal_length', 'petal_width']]
y = df.iloc['species']
Also, if you want to convert labels from string to numerical format use sklearn LabelEncoder
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
y = le.fit_transform(y)