I have a dataframe in python, called df. It contains two variables, Name and Age. I want to do a loop in python to generate 10 new column dataframes, called Age_1, Age_2, Age_3....Age_10 which contain the values of Age.
So far I have tried:
import pandas as pd
data = [['tom', 10], ['nick', 15], ['juli', 14]]
df = pd.DataFrame(data, columns = ['Name', 'Age'])
for i in range(1,11):
df[Age_'i'] = df['Age']
Just use this for loop:
for x in range(0,11):
df['Age_'+str(x)]=df['Age']
OR
for x in range(0,11):
df['Age_{}'.format(x)]=df['Age']
OR
for x in range(0,11):
df['Age_%s'%(x)]=df['Age']
Now if you print df you will get your desired output:
you can use .assign and ** unpacking.
df.assign(**{f'Age_{i}' : df['Age'] for i in range(11)})
Related
i have the following issue:
when i use .loc funtion it returns a series not a single value with no index.
As i need to do some math operation with the selected cells. the function that i am using is:
import pandas as pd
data = [[82,1], [30, 2], [3.7, 3]]
df = pd.DataFrame(data, columns = ['Ah-Step', 'State'])
df['Ah-Step'].loc[df['State']==2]+ df['Ah-Step'].loc[df['State']==3]
.values[0] will do what OP wants.
Assuming one wants to obtain the value 30, the following will do the work
df.loc[df['State'] == 2, 'Ah-Step'].values[0]
print(df)
[Out]: 30.0
So, in OP's specific case, the operation 30+3.7 could be done as follows
df.loc[df['State'] == 2, 'Ah-Step'].values[0] + df['Ah-Step'].loc[df['State']==3].values[0]
[Out]: 33.7
So want to determine what values are in a Pandas Dataframe:
import pandas as pd
d = {'col1': [1,2,3,4,5,6,7], 'col2': [3, 4, 3, 5, 7,22,3]}
df = pd.DataFrame(data=d)
col2 hast the unique values 3,4,5,6,22 (domain). Each value that exists shall be determined. But only once.
Is there anyway to fastly extract what the domain is in a Pandas Dataframe Column?
Use df.max() and df.min() to find the range.
print(df["col2"].unique())
by Andrej Kesely is the solution. Perfect!
I'm passing a pandas DataFrame containing various features to sklearn and I do not want the estimator to use the dataframe index as one of the features. Does sklearn use the index as one of the features?
df_features = pd.DataFrame(columns=["feat1", "feat2", "target"])
# Populate the dataframe (not shown here)
y = df_features["target"]
X = df_features.drop(columns=["target"])
estimator = RandomForestClassifier()
estimator.fit(X, y)
No, sklearn doesn't use the index as one of your feature. It essentially happens here, when you call the fit method the check_array function will be applied. And now if you dig deep into the check_array function, you can find that you are converting your input into array using np.array function which essentially strips the indices from your dataframe as shown below:
import pandas as pd
import numpy as np
data = [['tom', 10], ['nick', 15], ['juli', 14]]
df = pd.DataFrame(data, columns = ['Name', 'Age'])
df
Name Age
0 tom 10
1 nick 15
2 juli 14
np.array(df)
array([['tom', 10],
['nick', 15],
['juli', 14]], dtype=object)
I am new to pandas.I have a simple question:
how to extract the unique values and its count of a column and store in data frame with index key
I have tried to:
df = df1['Genre'].value_counts()
and I am getting a series but I don't know how to convert it to data frame object.
Pandas series has a .to_frame() function. Try it:
df = df1['Genre'].value_counts().to_frame()
And if you wanna "switch" the rows to columns:
df = df1['Genre'].value_counts().to_frame().T
Update: Full example if you want them as columns:
import pandas as pd
import numpy as np
np.random.seed(400) # To reproduce random variables
df1 = pd.DataFrame({
'Genre': np.random.choice(['Comedy','Drama','Thriller'], size=10)
})
df = df1['Genre'].value_counts().to_frame().T
print(df)
Returns:
Thriller Comedy Drama
Genre 5 3 2
try
df = pd.DataFrame(df1['Genre'].value_counts())
Let df1, df2, and df3 are pandas.DataFrame's having the same structure but different numerical values. I want to perform:
res=if df1>1.0: (df2-df3)/(df1-1) else df3
res should have the same structure as df1, df2, and df3 have.
numpy.where() generates result as a flat array.
Edit 1:
res should have the same indices as df1, df2, and df3 have.
For example, I can access df2 as df2["instanceA"]["parameter1"]["paramter2"]. I want to access the new calculated DataFrame/Series res as res["instanceA"]["parameter1"]["paramter2"].
Actually numpy.where should work fine there. Output here is 4x2 (same as df1, df2, df3).
df1 = pd.DataFrame( np.random.randn(4,2), columns=list('xy') )
df2 = pd.DataFrame( np.random.randn(4,2), columns=list('xy') )
df3 = pd.DataFrame( np.random.randn(4,2), columns=list('xy') )
res = df3.copy()
res[:] = np.where( df1 > 1, (df2-df3)/(df1-1), df3 )
x y
0 -0.671787 -0.445276
1 -0.609351 -0.881987
2 0.324390 1.222632
3 -0.138606 0.955993
Note that this should work on both series and dataframes. The [:] is slicing syntax that preserves the index and columns. Without that res will come out as an array rather than series or dataframe.
Alternatively, for a series you could write as #Kadir does in his answer:
res = pd.Series(np.where( df1>1, (df2-df3)/(df1-1), df3 ), index=df1.index)
Or similarly for a dataframe you could write:
res = pd.DataFrame(np.where( df1>1, (df2-df3)/(df1-1), df3 ), index=df1.index,
columns=df1.columns)
Integrating the idea in this question into JohnE's answer, I have come up with this solution:
res = pd.Series(np.where( df1 > 1, (df2-df3)/(df1-1), df3 ), index=df1.index)
A better answer using DataFrames will be appreciated.
Say df is your initial dataframe and res is the new column. Use a combination of setting values and boolean indexing.
Set res to be a copy of df3:
df['res'] = df['df3']
Then adjust values for your condition.
df[df['df1']>1.0]['res'] = (df['df2'] - df['df3'])/(df['df1']-1)