New answers tagged dataframe
0
Some suggested alternative plotting methods to visualise this data:
Histogram of the y-axis. Check the distribution of time intervals
df.plot.hist(by='interval', bins=10) #test varying the bin size
Plot smaller subsets of the data if the order is important e.g.
df[:100].plot() furthermore, if there is periodicity in the data, e.g. daily, hourly etc. you ...
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You have repeated jobs(JOB0), So its better to create a unique id for the jobs then pivot it based on id and job like
df['id'] = df.groupby(['job', 'result']).cumcount()
df2 = df.pivot_table(index=['id','job'], columns='result', values='time')
Output:
result END START
id job
0 JOB0 5209 1357
JOB2 2379 2405
JOB3 6578 4010
1 ...
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You should be able to use pandas.DataFrame.pivot for this as follows:
import pandas as pd
df2 = df.pivot(index="job", columns="result", values="time")
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If you just multiply the two dataframes, the missing rows will be filled with NaN values (missing values. You can then simply replace all these with 0.0, or any value.
Here is an example:
In [1]: import pandas as pd
In [2]: df1 = pd.DataFrame(range(6), columns=["A"])
In [3]: df2 = pd.DataFrame(range(8), columns=["A"]) # different ...
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How about a lambda using df.apply? So,
import pandas as pd
import numpy as np
#initializing variable names
variable_names =['var_' + str(i) for i in range(1, 15, 1)]
variable_names.insert(0, 'age')
Generating random data
data =pd.DataFrame(np.random.randint(0,100,size=(100, len(variable_names))), columns=variable_names)
# 5 bins
data['age_bins'] =pd.cut(...
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I think first converting the data from a wide to long format should allow you to sort the variables and get the result you want. This would look something like this:
import pandas as pd
# create example df
df = pd.DataFrame({
"age_bin": ["0-10", "10-20", "20-30"],
"col1": [5, 4, 2],
"col2&...
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The easiest way to do it for your case is something like this:
In [1]: import pandas as pd
In [2]: df1 = pd.DataFrame([0, 1, 2, 3, 1, 2, 3, 1, 2], columns=["A"])
In [3]: df2 = pd....
0
First, you need to convert list of list into a list.
From link, you can convert a list of lists into a list by declaring the following function.
flatten = lambda t: [item for sublist in t for item in sublist]
Now all you need is to create dataframe using created lists.
data = {"Newspaper":flatten(newspaper), "Title": flatten(title), &...
1
You may use
df.groupby(['BirthDate', 'ZipCode']).size().reset_index().rename(columns={0: 'n'})
and now you have a data frame that you can easily manipulate.
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First answers is close, the only thing you need to do is merge both data frames using two fields. You do not need both data frames to have the same length at all
Try:
pd.merge(df1, df2, on = ["number","trans"], how = "left")
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