Is there a way to print SchemaErrors when using pa.check_inputs? say i have df below
import pandera as pa
import pandas as pd
df = pd.DataFrame.from_dict({
'a' : [1,2,2,4,5],
'b' : [1,2,3,4,'dogs'],
})
schema = pa.DataFrameSchema({
'a': pa.Column(
pa.Int64,
checks=[pa.Check.isin([1,2,3,4,5])]),
'b': pa.Column(
pa.Int64,
checks=[pa.Check.isin([1,2,3,4,5])]),
})
if I where to run foo
#pa.check_input(schema, lazy=True)
def foo(df : pd.DataFrame) -> int:
return df.b.count()
foo(df)
the output would look like so:
Error Counts
------------
- schema_component_check: 2
Schema Error Summary
--------------------
failure_cases n_failure_cases
schema_context column check
Column b dtype('int64') [object] 1
isin({1, 2, 3, 4, 5}) [dogs] 1
Usage Tip
---------
however what I'd really would like to see is :
schema_context column check check_number failure_case index
0 Column b dtype('int64') None object None
1 Column b isin({1, 2, 3, 4, 5}) 0 dogs 4
which we get if we use try except.
try:
schema.validate(df, lazy=True)
except pa.errors.SchemaErrors as err:
print( err.failure_cases ) # dataframe of schema errors
Related
I have the issue with groupby and apply
df = pd.DataFrame({'A': ['a', 'a', 'a', 'b', 'b', 'b', 'b'], 'B': np.r_[1:8]})
I want to create a column C for each group take value 1 if B > z_score=2 and 0 otherwise. The code:
from scipy import stats
df['C'] = df.groupby('A').apply(lambda x: 1 if np.abs(stats.zscore(x['B'], nan_policy='omit')) > 2 else 0, axis=1)
However, I am unsuccessful with code and cannot figure out the issue
Use GroupBy.transformwith lambda, function, then compare and for convert True/False to 1/0 convert to integers:
from scipy import stats
s = df.groupby('A')['B'].transform(lambda x: np.abs(stats.zscore(x, nan_policy='omit')))
df['C'] = (s > 2).astype(int)
Or use numpy.where:
df['C'] = np.where(s > 2, 1, 0)
Error in your solution is per groups:
from scipy import stats
df = df.groupby('A')['B'].apply(lambda x: 1 if np.abs(stats.zscore(x, nan_policy='omit')) > 2 else 0)
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
If check gotcha in pandas docs:
pandas follows the NumPy convention of raising an error when you try to convert something to a bool. This happens in an if-statement or when using the boolean operations: and, or, and not.
So if use one of solutions instead if-else:
from scipy import stats
df = df.groupby('A')['B'].apply(lambda x: (np.abs(stats.zscore(x, nan_policy='omit')) > 2).astype(int))
print (df)
A
a [0, 0, 0]
b [0, 0, 0, 0]
Name: B, dtype: object
but then need convert to column, for avoid this problems is used groupby.transform.
You can use groupby + apply a function that finds the z-scores of each item in each group; explode the resulting list; use gt to create a boolean series and convert it to dtype int
df['C'] = df.groupby('A')['B'].apply(lambda x: stats.zscore(x, nan_policy='omit')).explode(ignore_index=True).abs().gt(2).astype(int)
Output:
A B C
0 a 1 0
1 a 2 0
2 a 3 0
3 b 4 0
4 b 5 0
5 b 6 0
6 b 7 0
I have two tables
import pandas as pd
import numpy as np
df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
columns=['a', 'b', 'c'])
df1 = pd.DataFrame(np.array([[1, 2, 4], [4, 5, 6], [7, 8, 9]]),
columns=['a', 'b', 'c'])
print(df1.equals(df2))
I want to compare them. I want the same result if I would use function df.compare(df1) or at least something close to it. Can't use above fnction as my complier states that 'DataFrame' object has no attribute 'compare'
First approach:
Let's compare value by value:
In [1183]: eq_df = df1.eq(df2)
In [1196]: eq_df
Out[1200]:
a b c
0 True True False
1 True True True
2 True True True
Then let's reduce it down to see which rows are equal for all columns
from functools import reduce
In [1285]: eq_ser = reduce(np.logical_and, (eq_df[c] for c in eq_df.columns))
In [1288]: eq_ser
Out[1293]:
0 False
1 True
2 True
dtype: bool
Now we can print out the rows which are not equal
In [1310]: df1[~eq_ser]
Out[1315]:
a b c
0 1 2 4
In [1316]: df2[~eq_ser]
Out[1316]:
a b c
0 1 2 3
Second approach:
def diff_dataframes(
df1, df2, compare_cols=None
) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
"""
Given two dataframes and column(s) to compare, return three dataframes with rows:
- common between the two dataframes
- found only in the left dataframe
- found only in the right dataframe
"""
df1 = df1.fillna(pd.NA)
df = df1.merge(df2.fillna(pd.NA), how="outer", on=compare_cols, indicator=True)
df_both = df.loc[df["_merge"] == "both"].drop(columns="_merge")
df_left = df.loc[df["_merge"] == "left_only"].drop(columns="_merge")
df_right = df.loc[df["_merge"] == "right_only"].drop(columns="_merge")
tup = namedtuple("df_diff", ["common", "left", "right"])
return tup(df_both, df_left, df_right)
Usage:
In [1366]: b, l, r = diff_dataframes(df1, df2)
In [1371]: l
Out[1371]:
a b c
0 1 2 4
In [1372]: r
Out[1372]:
a b c
3 1 2 3
Third approach:
In [1440]: eq_ser = df1.eq(df2).sum(axis=1).eq(len(df1.columns))
Following reproducible script is intended to process each two rows in a dataframe with length 5. For each two processed rows, I like to print a list of items that have been processed.
import pandas as pd
import itertools
my_dict = {
'name' : ['a', 'b', 'c', 'd', 'e'],
'age' : [10, 20, 30, 40, 50]
}
df = pd.DataFrame(my_dict)
for index, row in itertools.islice(df.iterrows(), 2):
rowlist = (row.name)
print('Processed two rows {}'.format(rowlist))
Output:
a
b
I'm looking for a way to get following desired output:
[a,b]
[c,d]
[e]
Tried:
print(df.groupby(df.index//2)['name'].agg(list))
Out:
0 [a, b]
1 [c, d]
2 [e]
Name: name, dtype: object
0 [a, b]
1 [c, d]
2 [e]
Name: name, dtype: object
Thanks for your help!
I am trying to apply a function to every column in a dataframe, when I try to do it on just a single fixed column name it works. I tried doing it on every column, but when I try passing the column name as an argument in the function I get an error.
How do you properly pass arguments to apply a function on a data frame?
def result(row,c):
if row[c] >=0 and row[c] <=1:
return 'c'
elif row[c] >1 and row[c] <=2:
return 'b'
else:
return 'a'
cols = list(df.columns.values)
for c in cols
df[c] = df.apply(result, args = (c), axis=1)
TypeError: ('result() takes exactly 2 arguments (21 given)', u'occurred at index 0')
Input data frame format:
d = {'c1': [1, 2, 1, 0], 'c2': [3, 0, 1, 2]}
df = pd.DataFrame(data=d)
df
c1 c2
0 1 3
1 2 0
2 1 1
3 0 2
You don't need to pass the column name to apply. As you only want to check if values of the columns are in certain range and should return a, b or c. You can make the following changes.
def result(val):
if 0<=val<=1:
return 'c'
elif 1<val<=2:
return 'b'
return 'a'
cols = list(df.columns.values)
for c in cols
df[c] = df[c].apply(result)
Note that this will replace your column values.
A faster way is np.select:
import numpy as np
values = ['c', 'b']
for col in df.columns:
df[col] = np.select([0<=df[col]<=1, 1<df[col]<=2], values, default = 'a')
I am trying to play around with data analysis, taking in data from a simple CSV file I have created with random values in it.
I have defined a function that should allow the user to type in a value3 then from the dataFrame, plot a bar graph. The below:
def analysis_currency_pair():
x=raw_input("what currency pair would you like to analysie ? :")
print type(x)
global dataFrame
df1=dataFrame
df2=df1[['currencyPair','amount']]
df2 = df2.groupby(['currencyPair']).sum()
df2 = df2.loc[x].plot(kind = 'bar')
When I call the function, the code returns my question, along with giving the output of the currency pair. However, it doesn't seem to put x (the value input by the user) into the later half of the function, and so no graph is produced.
Am I doing something wrong here?
This code works when we just put the value in, and not within a function.
I am confused!
I think you need rewrite your function with two parameters: x and df, which are passed to function analysis_currency_pair:
import pandas as pd
df = pd.DataFrame({"currencyPair": pd.Series({1: 'EURUSD', 2: 'EURGBP', 3: 'CADUSD'}),
"amount": pd.Series({1: 2, 2: 2, 3: 3.5}),
"a": pd.Series({1: 7, 2: 8, 3: 9})})
print df
# a amount currencyPair
#1 7 2.0 EURUSD
#2 8 2.0 EURGBP
#3 9 3.5 CADUSD
def analysis_currency_pair(x, df1):
print type(x)
df2=df1[['currencyPair','amount']]
df2 = df2.groupby(['currencyPair']).sum()
df2 = df2.loc[x].plot(kind = 'bar')
#raw input is EURUSD or EURGBP or CADUSD
pair=raw_input("what currency pair would you like to analysie ? :")
analysis_currency_pair(pair, df)
Or you can pass string to function analysis_currency_pair:
import pandas as pd
df = pd.DataFrame({"currencyPair": [ 'EURUSD', 'EURGBP', 'CADUSD', 'EURUSD', 'EURGBP'],
"amount": [ 1, 2, 3, 4, 5],
"amount1": [ 5, 4, 3, 2, 1]})
print df
# amount amount1 currencyPair
#0 1 5 EURUSD
#1 2 4 EURGBP
#2 3 3 CADUSD
#3 4 2 EURUSD
#4 5 1 EURGBP
def analysis_currency_pair(x, df1):
print type(x)
#<type 'str'>
df2=df1[['currencyPair','amount']]
df2 = df2.groupby(['currencyPair']).sum()
print df2
# amount
#currencyPair
#CADUSD 3
#EURGBP 7
#EURUSD 5
df2 = df2.loc[x].plot(kind = 'bar')
analysis_currency_pair('CADUSD', df)