I am trying to label a data frame by on-peak, mid-peak, off-peak etc. I managed to get the values I want to assign in this 'Mid-Peak', df['Peak'][df['func'] == 'Winter_Weekend']. However, when I include the .between_time I get the error: SyntaxError: can't assign to function call. I am not sure how to fix this. My goal is for the code code to work like this. Do I need another function or a do I need to change the syntax? Thank you for the help.
df['Peak'][df['func'] == 'Winter_Weekend'].between_time('16:00','21:00', include_end=False) = 'Mid-Peak'
In general, you can't assign a result to a function call, so need a different syntax. You could try
selection = df[df['func'] == 'Winter_Weekend'].between_time('16:00','21:00', include_end=False)
selection["Peak"] = "Mid-Peak"
But this doesn't update your original df, only the rows copied into selection.
To update the original dataframe, one way is to use loc to select both rows and a column, and .index to apply the between_time selection to the original dataframe:
ww = df["func"] == "Winter_Weekend"
df.loc[df[ww].between_time('16:00', '21:00', include_end=False).index, "Peak"] = "Mid-Peak"
I would recommend leveraging np.where() here, as follows:
df['Peak'] = np.where(df[df['func'] == 'Winter_Weekend'].between_time('16:00','21:00', include_end=False), 'Mid-Peak', df['Peak'])
Related
I'm having difficulty applying my knowledge of defining functions with def to my own function.
I want to create a function where I can filter my data frame based on my 1. columns I'd like to drop + their axis 2. using .dropna
I've used it on one of my data frames like this :
total_adj_gross = ((gross.drop(columns = ['genre','rating', 'total_gross'], axis = 1)).dropna())
I've also used it on another data frame like this :
vill = (characters.drop(columns = ['hero','song'], axis = 1)).dropna(axis = 0)
Can I make a function using def so I can easily do this to any data frame?
if so would I go about it like this
def filtered_df(data, col_name, N=1):
frame = data.drop(columns = [col_name], axis = N)
frame.dropna(axis = N)
return frame
I can already feel like this above function would go wrong because what if I have different N's like in my vill object?
BTW I am a very new beginner as you can tell -- I haven't been exposed to any complex functions any help would be appreciated!
Update since I dont know how to make a code in comments:
Thank you all for your help in creating my function
but now how do I insert this in my code?
Do I have to make a script (.py) then call my function?
can I test in within my actual code?
right now if I just copy + paste any code in, and fill the column name I get an error saying the specific column code "is not found in the axis"
Based on what you want to achieve, you don't need to pass any axis parameter. Also, you want to pass a list of columns as a parameter to drop the different columns (axis=1 for drop() and axis=0 for dropna(), which is the default parameter value). And finally, dropna() is not in place by default. You have to store the returned value into a frame like you did the line above.
Your function should look like that:
def filtered_df(data, col_names):
frame = data.drop(columns = col_names, axis = 1)
result = frame.dropna()
return result
Overall, code looks good. I'd suggest 3 minor changes:-
Pass columns names as list. Do not convert them to list within the functions
Pass 2 variables for working with axis. From what i see in your eg, your axis values changes for drop and dropna. Not sure about your need for it. If you want 2 diff axis values for drop() and dropna() then please use 2 diff variables, probably like drop_axis and dropna_axis.
assigning modified frame / single line operation
So, code would look something like this:-
def filtered_df(data, col_name, drop_axis=1, dropna_axis=0):
frame = data.drop(columns = col_name, axis = drop_axis).dropna(axis = dropna_axis)
return frame
Your call to it can look like:
modified_df = filtered_df(data, ["x_col","y_col"], 0, 0)
I'm very new to Python and am having a problem trying to execute a very basic task in pandas. I am trying to create a new column (variable) called RACE which is based off of the values in RAC1P_RC1. I have tried every way to recode RACE (loc, apply, lambda), but it will not update its values at all, even when the argument is true. For example, I tried to use the code
def f(x):
if x['RAC1P_RC1'] == 1: return 1
else: return 0
acs['RACE'] = acs.apply(f, axis=1)
And when I look at the dataframe, all cases in RACE have a value of 0, even in cases where RAC1P_RC1 equals 1. There seems to be something very basic I'm missing here, since this is one of the simplest tasks in pandas, and I'm not able to do it. Any help would be appreciated.
Check the datatype of 'RAC1P_RC1' column, make sure it's not an object data type. If its object datatype then the condition (if x['RAC1P_RC1'] == 1) will always return False.
Also, you could use .loc to make the code faster as follows:
mask = (acs['RAC1P_RC1'] == 1)
acs.loc[mask,'RACE'] = 1
acs.loc[~mask,'RACE'] = 0
You can check your condition directly and that will give you a Series of True/False then typecast that Series to int via astype() method and you will get corrosponding binary values:
acs['RACE'] =acs['RAC1P_RC1'].eq(1).astype(int)
OR
you can also use view() method in place of astype() for achieving the same:
acs['RACE'] =acs['RAC1P_RC1'].eq(1).view('i1')
I have a function that aims at printing the sum along a column of a pandas DataFrame after filtering on some rows to be defined ; and the percentage this quantity makes up in the same sum without any filter:
def my_function(df, filter_to_apply, col):
my_sum = np.sum(df[filter_to_apply][col])
print(my_sum)
print(my_sum/np.sum(df[col]))
Now I am wondering if there is any way to have a filter_to_apply that actually doesn't do any filter (i.e. keeps all rows), to keep using my function (that is actually a bit more complex and convenient) even when I don't want any filter.
So, some filter_f1 that would do: df[filter_f1] = df and could be used with other filters: filter_f1 & filter_f2.
One possible answer is: df.index.isin(df.index) but I am wondering if there is anything easier to understand (e.g. I tried to use just True but it didn't work).
A Python slice object, i.e. slice(-1), acts as an object that selects all indexes in a indexable object. So df[slice(-1)] would select all rows in the DataFrame. You can store that in a variable an an initial value which you can further refine in your logic:
filter_to_apply = slice(-1) # initialize to select all rows
... # logic that may set `filter_to_apply` to something more restrictive
my_function(df, filter_to_apply, col)
This is a way to select all rows:
df[range(0, len(df))]
this is also
df[:]
But I haven't figured out a way to pass : as an argument.
Theres a function called loc on pandas that filters rows. You could do something like this:
df2 = df.loc[<Filter here>]
#Filter can be something like df['price']>500 or df['name'] == 'Brian'
#basically something that for each row returns a boolean
total = df2['ColumnToSum'].sum()
My dataframe has a column contains various type values, I want to get the most counted one:
In this case, I want to get the label FM-15, so later on I can query data only labled by this.
How can I do that?
Now I can get away with:
most_count = df['type'].value_counts().max()
s = df['type'].value_counts()
s[s == most_count].index
This returns
Index([u'FM-15'], dtype='object')
But I feel this is to ugly, and I don't know how to use this Index() object to query df. I only know something like df = df[(df['type'] == 'FM-15')].
Use argmax:
lbl = df['type'].value_counts().argmax()
To query,
df.query("type==#lbl")
In Python's Pandas, I am using the Data Frame as such:
drinks = pandas.read_csv(data_url)
Where data_url is a string URL to a CSV file
When indexing the frame for all "light drinkers" where light drinkers is constituted by 1 drink, the following is written:
drinks.light_drinker[drinks.light_drinker == 1]
Is there a more DRY-like way to self-reference the "parent"? I.e. something like:
drinks.light_drinker[self == 1]
You can now use query or assign depending on what you need:
drinks.query('light_drinker == 1')
or to mutate the the df:
df.assign(strong_drinker = lambda x: x.light_drinker + 100)
Old answer
Not at the moment, but an enhancement with your ideas is being discussed here. For simple cases where might be enough. The new API might look like this:
df.set(new_column=lambda self: self.light_drinker*2)
In the most current version of pandas, .where() also accepts a callable!
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.where.html?highlight=where#pandas.DataFrame.where
So, the following is now possible:
drinks.light_drinker.where(lambda x: x == 1)
which is particularly useful in method-chains. However, this will return only the Series (not the DataFrame filtered based on the values in the light_drinker column). This is consistent with your question, but I will elaborate for the other case.
To get a filtered DataFrame, use:
drinks.where(lambda x: x.light_drinker == 1)
Note that this will keep the shape of the self (meaning you will have rows where all entries will be NaN, because the condition failed for the light_drinker value at that index).
If you don't want to preserve the shape of the DataFrame (i.e you wish to drop the NaN rows), use:
drinks.query('light_drinker == 1')
Note that the items in DataFrame.index and DataFrame.columns are placed in the query namespace by default, meaning that you don't have to reference the self.
I don't know of any way to reference parent objects like self or this in Pandas, but perhaps another way of doing what you want which could be considered more DRY is where().
drinks.where(drinks.light_drinker == 1, inplace=True)