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I got a dictionary with 3 Dataframes {0: DataFrame, 1: DataFrame, 2: DataFrame}.
Each DataFrame has the same size. 6 variables, 25 rows.
I'd like to sum all the values/rows from each DataFrame column 'Income' and pass the sum to a list.
Looking like this
list_of_sums = [Sum of income DF0, Sum of income DF1, Sum of income DF2]
Try this below :
list_of_sums = []
input_dict = {0: DataFrame, 1: DataFrame, 2: DataFrame}
for obj in input_dict:
list_of_sums.append(input_dict[obj]['Income'].sum()) # use sum function and append the result to the output list.
print(list_of_sums)
Not the most optimized one but this works.
import pandas as pd
d, dd, ddd = pd.DataFrame(),pd.DataFrame(),pd.DataFrame()
d['Income'] = [3,4,5,6]
dd['Income'] = [30,40,50,60]
ddd['Income'] = [300,400,500,600]
dicts = [d,dd,ddd]
for dc in dicts:
print(sum([s for s in dc['Income']]))
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How can I generate a random dataset with 8 columns and 50,000 rows. Each column must be a categorical variable (breed of dog) with 3 levels (example, colour), resulting in approximately equal proportions using Python. How many unique rows (i.e., permutations of category levels) are possible?
data = np.empty(50000, 8)
data[:,0] = [np.random.choice(col0_options) for i in range(50000)]
# do this for all columns ...
data[:,7] = [np.random.choice(col7_options) for i in range(50000)]
If you want to make it less hardcoded you could store every list of options in a dict like {0: col0_options, ..., 7: col7_options} and then do:
options = {0: col0_options, ..., 7: col7_options}
data = np.empty(50000, 8)
for i in range(8):
data[:,i] = [np.random.choice(options[i]) for i in range(50000)]
Since there are 8 cols and 3 values per col there are 3^8 unique possibilities.
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I have a dataframe in which each row show one transaction, items purchased together. Here is how my dataframe looks like
items
['A','B','C']
['A','C]
['C','F']
...
I need to create a dictionary which shows how many times items have been purchased together, something like below
{'A':[('B',1),('C':5)], 'B': [('A':1),('C':6)], ...}
Right now, I have defined a variable freq and then loop through my dataframe and calculate/update the dictionary (freq). but it's taking very long.
What's the efficient way of calculating this without looping through the dataframe?
You can speed this up with sklearn's MultiLabelBinarizer:
from sklearn.preprocessing import MultiLabelBinarizer
Transform your data using:
mlb = MultiLabelBinarizer()
df = pd.DataFrame(mlb.fit_transform(df['items']),
columns=mlb.classes_,
index=df.index)
to get it in the following format:
A B C F
0 1 1 1 0
1 1 0 1 0
2 0 0 1 1
And then getting you can define a trivial function like:
get_num_coexisting = lambda x, y: (df[x] & df[y]).sum()
And use as so:
get_num_coexisting('A', 'C')
>>> 2
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I have the user input two lists, one for sizes one for minutes they are each stored in a list. For example they can input sizes: 111, 121 and for minutes, 5, 10, 15.
I want to have the dataframe have columns that are named by the size and minute. (I did a for loop to extract each size and minute.) For example I want the columns to say 111,5 ; 111,10; 111;15, etc. I tried to do df[size+minute]=values (Values is data I want to input into each column) but instead the column name is just the values added up so I got the column name to be 116 instead of 111,5.
If you have two lists:
l = [111,121]
l2 = [5,10,15]
Then you can use list comprehension to form your column names:
col_names = [str(x)+';'+str(y) for x in l for y in l2]
print(col_names)
['111;5', '111;10', '111;15', '121;5', '121;10', '121;15']
And create a dataframe with these column names using pandas.DataFrame:
df = pd.DataFrame(columns=col_names)
If we add a row of data:
row = pd.DataFrame([[1,2,3,4,5,6]])
row.columns = col_names
df = df.append(pd.DataFrame(row))
We can see that our dataframe looks like this:
print(df)
111;5 111;10 111;15 121;5 121;10 121;15
0 1 2 3 4 5 6
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I have a Panda and want to do a calculation based on an existing column.
However, the apply. function is not working for some reason.
It's something like letssay
df = pd.DataFrame({'Age': age, 'Input': input})
and the input column is something like [1.10001, 1.49999, 1.60001]
Now I want to add a new column to the Dataframe, that is doing the following:
Add 0.0001 to each element in column
Multiply each value by 10
Transform each value of new column to int
Use Series.add, Series.mul and Series.astype:
#input is python code word (builtin), so better dont use it like variable
inp = [1.10001, 1.49999, 1.60001]
age = [10,20,30]
df = pd.DataFrame({'Age': age, 'Input': inp})
df['new'] = df['Input'].add(0.0001).mul(10).astype(int)
print (df)
Age Input new
0 10 1.10001 11
1 20 1.49999 15
2 30 1.60001 16
You could make a simple function and then apply it by row.
def f(row):
return int((row['input']+0.0001)*10))
df['new'] = df.apply(f, axis=1)
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I have a dataframe df with 3 columns :
df=pd.DataFrame({
'User':['A','A','B','A','C','B','C'],
'Values':['x','y','z','p','q','r','s'],
'Date':[14,11,14,12,13,10,14]
})
I want to create a new dataframe that will contain the rows corresponding to highest values in the 'Date' columns for each user. For example for the above dataframe I want the desired dataframe to be as follows ( its a jpeg image):
Can anyone help me with this problem?
This answer assumes that there is different maximum values per user in Values column:
In [10]: def get_max(group):
...: return group[group.Date == group.Date.max()]
...:
In [12]: df.groupby('User').apply(get_max).reset_index(drop=True)
Out[12]:
Date User Values
0 14 A x
1 14 B z
2 14 C s