I have two dataframe with the same size and same variables.
df1 size: (50, 3)
df2 size: (50, 3)
In my code, I'm using np.isclose function to return a boolean array where two arrays are element-wise equal within a tolerance as follows:
feature_1 = np.isclose(df1["SepalLengthCm"], df2["SepalLengthCm"], atol=10)
feature_2 = np.isclose(df1["SepalWidthCm"], df2["SepalWidthCm"], atol=20)
feature_3 = np.isclose(df1["PetalLengthCm"], df2["PetalLengthCm"], atol=30)
The code is working ok without any error.
But the issue for me is that I want to make this process more general and automatic. In other word, it should be working with any other datasets.
The important thing is that I dont want to specify the columns name in the code. So I want use for loop to automatically iterate over each column and doing the same thing.
Instead of having three codes (lines) for each column like feature_1 feature_2 feature_3, I want to write one code (line) to do the same job for any number of columns. Something like this:
feature = np.isclose(df1[columnn], df2[columnn], atol=i)
the parameter for atol should be also predefined in advance, for example i = [10, 20, 30]
You could define a function containing the loop you want, and call it as a one-liner:
def compare_isclose(df1, df2, atol_list):
feature_list = []
for i, col in enumerate(df1.columns):
feature_list.append(np.isclose(df1[col], df2[col], atol=atol_list[i]))
feature_df = pd.DataFrame.from_records(feature_list).T
return feature_df
The function can be used this way:
atol_list = [10,20,30]
feature_df = compare_isclose(df1, df2, atol_list)
It only works under the assumption that the number of elements in atol_list equals the number of columns in the dataframe, and that the column names of the 2 dataframes are identical.
Related
I have a problem with coloring dataframe and exporting the df to excel. I have two df's with same shape and index. In the first are only numbers 0, 1, 2 and 3. The second contains various numbers and strings.
What I want is to color the second df based on numbers in the first df. For that purpose I made two function as you can see bellow.
def apply_color(x):
colors = {0:'transparent',1: 'grey',2: 'yellow', 3: 'red'}
return df1.applymap(lambda val: 'background-color: {}'.format(colors.get(val,'')))
def coloring(dfInt,df):
df1 = dfInt
df2 = df.style.apply(apply_color, axis=None)
return df2
I have a big df, inside it I stored some info and two other df's on position 7 (df with numbers) and 8 (df with various numbers and strings).
x=0
a=[]
writer=pd.ExcelWriter("%s.xlsx"%file)
for i in df["Dimension"]:
dfExport = df.iat[x,8]
dfExportColor = df.iat[x,7]
sheet_name = i
# a.append(dfExportColor)
# a.append(dfExport)
dfa = coloring(dfExportColor,dfExport)
dfa.to_excel(writer,sheet_name=sheet_name)
x += 1
writer.save()
If I start the code, first three loops are OK. On the fourth it gives me ValueError
ValueError: Function <function apply_color at 0x0000025A60925990> created invalid index labels.
Usually, this is the result of the function returning a DataFrame which contains invalid labels, or returning an incorrectly shaped, list-like object which cannot be mapped to labels, possibly due to applying the function along the wrong axis.
Result index has shape: (1232,)
Expected index shape: (28484,)
But! I added into the code list "a" that contain all the df's. If I maunually use the last two (those that caused the error), the code works!
df1 = a[6]
df2 = a[7]
x = coloring(df1,df2)
writer=pd.ExcelWriter("x.xlsx")
x.to_excel(writer)
writer.save()
And in this moment, if I restart the for loop, it drops in the first loop. Then, if I again use the "manual" code for the df's from the loop, it works. And now, if I again restart the for loop, it drops again in the fourth lopp, and so on.
I am trying to fix it for the last 24 hours and I have no idea what more I can do.
Please, does anyone know how to fix it?
I am a new coder using jupyter notebook. I have a dataframe that contains 23 columns with different amounts of values( at most 23 and at least 2) I have created a function that normalizes the contents of one column below.
def normalize(column):
y = DFref[column].values[()]
y = x.astype(int)
KGF= list()
for element in y:
element_norm = element / x.sum()
KGF.append(element_norm)
return KGF
I am now trying to create a function that loops through all columns in the Data frame. Right now if I plug in the name of one column, it works as intended. What would I need to do in order to create a function that loops through each column and normalizes the values of each column, and then adds it to a new dataframe?
It's not clear if all 23 columns are numeric, but I will assume they are. Then there are a number of ways to solve this. The method below probably isn't the best, but it might be a quick fix for you...
colnames = DFref.columns.tolist()
normalised_data = {}
for colname in colnames:
normalised_data[colname] = normalize(colname)
df2 = pd.DataFrame(normalised_data)
I’ve been struggling the past week trying to use apply to use functions over an entire pandas dataframe, including rolling windows, groupby, and especially multiple input columns and multiple output columns. I found a large amount of questions on SO about this topic and many old & outdated answers. So I started to create a notebook for every possible combination of x inputs & outputs, rolling, rolling & groupby combined and I focused on performance as well. Since I’m not the only one struggling with these questions I thought I’d provide my solutions here with working examples, hoping it helps any existing/future pandas-users.
Important notes
The combination of apply & rolling in pandas has a very strong output requirement. You have to return one single value. You can not return a pd.Series, not a list, not an array, not secretly an array within an array, but just one value, e.g. one integer. This requirement makes it hard to get a working solution when trying to return multiple outputs for multiple columns. I don’t understand why it has this requirement for 'apply & rolling', because without rolling 'apply' doesn’t have this requirement. Must be due to some internal pandas functions.
The combination of 'apply & rolling' combined with multiple input columns simply does not work! Imagine a dataframe with 2 columns, 6 rows and you want to apply a custom function with a rolling window of 2. Your function should get an input array with 2x2 values - 2 values of each column for 2 rows. But it seems pandas can’t handle rolling and multiple input columns at the same time. I tried to use the axis parameter to get it working but:
Axis = 0, will call your function per column. In the dataframe described above, it will call your function 10 times (not 12 because rolling=2) and since it’s per column, it only provides the 2 rolling values of that column…
Axis = 1, will call your function per row. This is what you probably want, but pandas will not provide a 2x2 input. It actually completely ignores the rolling and only provides one row with values of 2 columns...
When using 'apply' with multiple input columns, you can provide a parameter called raw (boolean). It’s False by default, which means the input will be a pd.Series and thus includes indexes next to the values. If you don’t need the indexes you can set raw to True to get a Numpy array, which often achieves a much better performance.
When combining 'rolling & groupby', it returns a multi-indexes series which can’t easily serve as an input for a new column. The easiest solution is to append a reset_index(drop=True) as answered & commented here (Python - rolling functions for GroupBy object).
You might ask me, when would you ever want to use a rolling, groupby custom function with multiple outputs!? Answer: I recently had to do a Fourier transform with sliding windows (rolling) over a dataset of 5 million records (speed/performance is important) with different batches within the dataset (groupby). And I needed to save both the power & phase of the Fourier transform in different columns (multiple outputs). Most people probably only need some of the basic examples below, but I believe that especially in the Machine Learning/Data-science sectors the more complex examples can be useful.
Please let me know if you have even better, clearer or faster ways to perform any of the solutions below. I'll update my answer and we can all benefit!
Code examples
Let’s create a dataframe first that will be used in all the examples below, including a group-column for the groupby examples.
For the rolling window and multiple input/output columns I just use 2 in all code examples below, but obviously this could be any number > 1.
df = pd.DataFrame(np.random.randint(0,5,size=(6, 2)), columns=list('ab'))
df['group'] = [0, 0, 0, 1, 1, 1]
df = df[['group', 'a', 'b']]
It will look like this:
group a b
0 0 2 2
1 0 4 1
2 0 0 4
3 1 0 2
4 1 3 2
5 1 3 0
Input 1 column, output 1 column
Basic
def func_i1_o1(x):
return x+1
df['c'] = df['b'].apply(func_i1_o1)
Rolling
def func_i1_o1_rolling(x):
return (x[0] + x[1])
df['d'] = df['c'].rolling(2).apply(func_i1_o1_rolling, raw=True)
Roling & Groupby
Add the reset_index solution (see notes above) to the rolling function.
df['e'] = df.groupby('group')['c'].rolling(2).apply(func_i1_o1_rolling, raw=True).reset_index(drop=True)
Input 2 columns, output 1 column
Basic
def func_i2_o1(x):
return np.sum(x)
df['f'] = df[['b', 'c']].apply(func_i2_o1, axis=1, raw=True)
Rolling
As explained in point 2 in the notes above, there isn't a 'normal' solution for 2 inputs. The workaround below uses the 'raw=False' to ensure the input is a pd.Series, which means we also get the indexes next to the values. This enables us to get values from other columns at the correct indexes to be used.
def func_i2_o1_rolling(x):
values_b = x
values_c = df.loc[x.index, 'c'].to_numpy()
return np.sum(values_b) + np.sum(values_c)
df['g'] = df['b'].rolling(2).apply(func_i2_o1_rolling, raw=False)
Rolling & Groupby
Add the reset_index solution (see notes above) to the rolling function.
df['h'] = df.groupby('group')['b'].rolling(2).apply(func_i2_o1_rolling, raw=False).reset_index(drop=True)
Input 1 column, output 2 columns
Basic
You could use a 'normal' solution by returning pd.Series:
def func_i1_o2(x):
return pd.Series((x+1, x+2))
df[['i', 'j']] = df['b'].apply(func_i1_o2)
Or you could use the zip/tuple combination which is about 8 times faster!
def func_i1_o2_fast(x):
return x+1, x+2
df['k'], df['l'] = zip(*df['b'].apply(func_i1_o2_fast))
Rolling
As explained in point 1 in the notes above, we need a workaround if we want to return more than 1 value when using rolling & apply combined. I found 2 working solutions.
1
def func_i1_o2_rolling_solution1(x):
output_1 = np.max(x)
output_2 = np.min(x)
# Last index is where to place the final values: x.index[-1]
df.at[x.index[-1], ['m', 'n']] = output_1, output_2
return 0
df['m'], df['n'] = (np.nan, np.nan)
df['b'].rolling(2).apply(func_i1_o2_rolling_solution1, raw=False)
Pros: Everything is done within 1 function.
Cons: You have to create the columns first and it is slower since it doesn't use the raw input.
2
rolling_w = 2
nan_prefix = (rolling_w - 1) * [np.nan]
output_list_1 = nan_prefix.copy()
output_list_2 = nan_prefix.copy()
def func_i1_o2_rolling_solution2(x):
output_list_1.append(np.max(x))
output_list_2.append(np.min(x))
return 0
df['b'].rolling(rolling_w).apply(func_i1_o2_rolling_solution2, raw=True)
df['o'] = output_list_1
df['p'] = output_list_2
Pros: It uses the raw input which makes it about twice as fast. And since it doesn't use indexes to set the output values the code looks a bit more clear (to me at least).
Cons: You have to create the nan-prefix yourself and it takes a bit more lines of code.
Rolling & Groupby
Normally, I would use the faster 2nd solution above. However, since we're combining groups and rolling this means you'd have to manually set NaN's/zeros (depending on the number of groups) at the right indexes somewhere in the middle of the dataset. To me it seems that when combining rolling, groupby and multiple output columns, the first solution is easier and solves the automatic NaNs/grouping automatically. Once again, I use the reset_index solution at the end.
def func_i1_o2_rolling_groupby(x):
output_1 = np.max(x)
output_2 = np.min(x)
# Last index is where to place the final values: x.index[-1]
df.at[x.index[-1], ['q', 'r']] = output_1, output_2
return 0
df['q'], df['r'] = (np.nan, np.nan)
df.groupby('group')['b'].rolling(2).apply(func_i1_o2_rolling_groupby, raw=False).reset_index(drop=True)
Input 2 columns, output 2 columns
Basic
I suggest using the same 'fast' way as for i1_o2 with the only difference that you get 2 input values to use.
def func_i2_o2(x):
return np.mean(x), np.median(x)
df['s'], df['t'] = zip(*df[['b', 'c']].apply(func_i2_o2, axis=1))
Rolling
As I use a workaround for applying rolling with multiple inputs and I use another workaround for rolling with multiple outputs, you can guess I need to combine them for this one.
1. Get values from other columns using indexes (see func_i2_o1_rolling)
2. Set the final multiple outputs on the correct index (see func_i1_o2_rolling_solution1)
def func_i2_o2_rolling(x):
values_b = x.to_numpy()
values_c = df.loc[x.index, 'c'].to_numpy()
output_1 = np.min([np.sum(values_b), np.sum(values_c)])
output_2 = np.max([np.sum(values_b), np.sum(values_c)])
# Last index is where to place the final values: x.index[-1]
df.at[x.index[-1], ['u', 'v']] = output_1, output_2
return 0
df['u'], df['v'] = (np.nan, np.nan)
df['b'].rolling(2).apply(func_i2_o2_rolling, raw=False)
Rolling & Groupby
Add the reset_index solution (see notes above) to the rolling function.
def func_i2_o2_rolling_groupby(x):
values_b = x.to_numpy()
values_c = df.loc[x.index, 'c'].to_numpy()
output_1 = np.min([np.sum(values_b), np.sum(values_c)])
output_2 = np.max([np.sum(values_b), np.sum(values_c)])
# Last index is where to place the final values: x.index[-1]
df.at[x.index[-1], ['w', 'x']] = output_1, output_2
return 0
df['w'], df['x'] = (np.nan, np.nan)
df.groupby('group')['b'].rolling(2).apply(func_i2_o2_rolling_groupby, raw=False).reset_index(drop=True)
I have two dataframes,
a = {'SEX':[...], 'ENT':[...], 'XY':[...], 'RZD':[...], 'TOT':[...]} with shape 769, 5
and
b = {'K':[...], 'NOM':[...], 'M':[...], SEX':[...], 'ENT':[...], 'POB':[...], 'RZD':[...], '%A':[...], '%B':[...]} with shape 34398, 9.
I need to merge these dataframes based on 'SEX', 'ENT', 'RZD'. Once merged, I fill with zeros wherever values do not match. Finally, I calculate a new column FINAL that equals a['%A'] * b['TOT'] like the code below:
local = b.merge(a, on=['ENT', 'RZD', 'SEX'], how='left')
local.fillna(0, inplace=True)
local['TOT'] = local['%A'].mul(local['TOT']).round(0)
The problem I am encountering is that
x1 = a['TOT'].sum()
should be equal to
x2 = local['TOT'].sum()
However, I am getting differences of nearly 6 million. This means that x2 >> x1
Do you recommend any way of merging these dataframes and keep consistency?
You can find the raw files here.
Try df.join, and see if the how argument helps you out. The how argument will alert you to where the match was made, and instead of merging by conditions you might be able to do a vector operation over the column using
if df["column"] == "left_only":
df["column"] = df["column"].str.replace("left_only", 0)
# repeat
I have some DataFrames with information about some elements, for instance:
my_df1=pd.DataFrame([[1,12],[1,15],[1,3],[1,6],[2,8],[2,1],[2,17]],columns=['Group','Value'])
my_df2=pd.DataFrame([[1,5],[1,7],[1,23],[2,6],[2,4]],columns=['Group','Value'])
I have used something like dfGroups = df.groupby('group').apply(my_agg).reset_index(), so now I have DataFrmaes with informations on groups of the previous elements, say
my_df1_Group=pd.DataFrame([[1,57],[2,63]],columns=['Group','Group_Value'])
my_df2_Group=pd.DataFrame([[1,38],[2,49]],columns=['Group','Group_Value'])
Now I want to clean my groups according to properties of their elements. Let's say that I want to discard groups containing an element with Value greater than 16. So in my_df1_Group, there should only be the first group left, while both groups qualify to stay in my_df2_Group.
As I don't know how to get my_df1_Group and my_df2_Group from my_df1 and my_df2 in Python (I know other languages where it would simply be name+"_Group" with name looping in [my_df1,my_df2], but how do you do that in Python?), I build a list of lists:
SampleList = [[my_df1,my_df1_Group],[my_df2,my_df2_Group]]
Then, I simply try this:
my_max=16
Bad=[]
for Sample in SampleList:
for n in Sample[1]['Group']:
df=Sample[0].loc[Sample[0]['Group']==n] #This is inelegant, but trying to work
#with Sample[1] in the for doesn't work
if (df['Value'].max()>my_max):
Bad.append(1)
else:
Bad.append(0)
Sample[1] = Sample[1].assign(Bad_Row=pd.Series(Bad))
Sample[1] = Sample[1].query('Bad_Row == 0')
Which runs without errors, but doesn't work. In particular, this doesn't add the column Bad_Row to my df, nor modifies my DataFrame (but the query runs smoothly even if Bad_Rowcolumn doesn't seem to exist...). On the other hand, if I run this technique manually on a df (i.e. not in a loop), it works.
How should I do?
Based on your comment below, I think you are wanting to check if a Group in your aggregated data frame has a Value in the input data greater than 16. One solution is to perform a row-wise calculation using a criterion of the input data. To accomplish this, my_func accepts a row from the aggregated data frame and the input data as a pandas groupby object. For each group in your grouped data frame, it will subset you initial data and use boolean logic to see if any of the 'Values' in your input data meet your specified criterion.
def my_func(row,grouped_df1):
if (grouped_df1.get_group(row['Group'])['Value']>16).any():
return 'Bad Row'
else:
return 'Good Row'
my_df1=pd.DataFrame([[1,12],[1,15],[1,3],[1,6],[2,8],[2,1],[2,17]],columns=['Group','Value'])
my_df1_Group=pd.DataFrame([[1,57],[2,63]],columns=['Group','Group_Value'])
grouped_df1 = my_df1.groupby('Group')
my_df1_Group['Bad_Row'] = my_df1_Group.apply(lambda x: my_func(x,grouped_df1), axis=1)
Returns:
Group Group_Value Bad_Row
0 1 57 Good Row
1 2 63 Bad Row
Based on dubbbdan idea, there is a code that works:
my_max=16
def my_func(row,grouped_df1):
if (grouped_df1.get_group(row['Group'])['Value']>my_max).any():
return 1
else:
return 0
SampleList = [[my_df1,my_df1_Group],[my_df2,my_df2_Group]]
for Sample in SampleList:
grouped_df = Sample[0].groupby('Group')
Sample[1]['Bad_Row'] = Sample[1].apply(lambda x: my_func(x,grouped_df), axis=1)
Sample[1].drop(Sample[1][Sample[1]['Bad_Row']!=0].index, inplace=True)
Sample[1].drop(['Bad_Row'], axis = 1, inplace = True)