Compare multiple dataframes' columns names with original one's in Pandas - python-3.x

Question

Let's say I have a dataframe df with headers a, b, c, d.
I want to compare other dfs (df1, df2, df3, ...) columns name with it. I need all the dfs's columns name should be exactly identical as df (Please note the different order of columns names should be not considered as different column names).
For example:
Original dataframe:
df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
columns=['a', 'b', 'c'])
col = ['a', 'b', 'c']
dfs:
df1 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
columns=['a', 'c', 'b'])
Returns identical columns name;
df2 = pd.DataFrame(np.array([[1, 2, 3, 10], [4, 5, 6, 11], [7, 8, 9, 12]]),
columns=['a', 'c', 'e', 'b'])
Returns extra columns in dataframe;
df3 = pd.DataFrame(np.array([[1, 2], [4, 5], [7, 8]]),
columns=['a', 'c'])
Returns missing columns in dataframe;
df4 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
columns=['a', '*c', 'b'])
Returns errors in dataframe's column names;
df5 = pd.DataFrame(np.array([[1, 2, 3, 9], [4, 5, 6, 9], [7, 8, 9, 10]]),
columns=['a', 'b', 'b', 'c'])
returns extra columns in dataframe.
If it's too complicated, it's also OK returning columns names are incorrect for all kinds of errors.
How could I do that in Pandas? Thanks.

Answer 1

I think set here is good choice, because order is not important:
def compare(df, df1):
orig = set(df.columns)
c = set(df1.columns)
#testing if length of set is same like length of columns names
if len(c) != len(df1.columns):
return ('extra columns in dataframe')
#if same sets
elif (c == orig):
return ('identical columns name')
#compared subsets
elif c.issubset(orig):
return ('missing columns in dataframe')
#compared subsets
elif orig.issubset(c):
return ('extra columns in dataframe')
else:
return ('columns names are incorrect')
print(compare(df, df1))
print(compare(df, df2))
print(compare(df, df3))
print(compare(df, df4))
print(compare(df, df5))
identical columns name
extra columns in dataframe
missing columns in dataframe
columns names are incorrect
extra columns in dataframe
For returned values:
def compare(df, df1):
orig = set(df.columns)
c = set(df1.columns)
#testing if length of set is same like length of columns names
if len(c) != len(df1.columns):
col = df1.columns.tolist()
a = set([str(x) for x in col if col.count(x) > 1])
return f'duplicated columns: {", ".join(a)}'
#if same sets
elif (c == orig):
return ('identical columns name')
#compared subsets
elif c.issubset(orig):
a = (str(x) for x in orig - c)
return f'missing columns: {", ".join(a)}'
#compared subsets
elif orig.issubset(c):
a = (str(x) for x in c - orig)
return f'extra columns: {", ".join(a)}'
else:
a = (str(x) for x in c - orig)
return f'incorrect: {", ".join(a)}'
print(compare(df, df1))
print(compare(df, df2))
print(compare(df, df3))
print(compare(df, df4))
print(compare(df, df5))
identical columns name
extra columns: e
missing columns: b
incorrect: *c
duplicated columns: b

Answer 2

I wrote a normal python function which uses pandas function to get columns and compare them, please see if this helps:
def check_errors(original_df, df1):
original_columns = original_df.columns
columns1 = df1.columns
if len(original_columns) > len(columns1):
print("Columns missing!!")
elif len(original_columns) < len(columns1):
print("Extra Columns")
else:
for i in columns1:
if i not in original_columns:
print("Column names are incorrect")