Split rows with same ID into different columns python - python-3.x

I want to have a dataframe with repeated values with the same id number. But i want to split the repeated rows into colunms.
data = [[10450015,4.4],[16690019 4.1],[16690019,4.0],[16510069 3.7]]
df = pd.DataFrame(data, columns = ['id', 'k'])
print(df)
The resulting dataframe would have n_k (n= repated values of id rows). The repeated id gets a individual colunm and when it does not have repeated id, it gets a 0 in the new colunm.
data_merged = {'id':[10450015,16690019,16510069], '1_k':[4.4,4.1,3.7], '2_k'[0,4.0,0]}
print(data_merged)

Try assiging the column idx ref, using DataFrame.assign and groupby.cumcount then DataFrame.pivot_table. Finally use a list comprehension to sort column names:
df_new = (df.assign(col=df.groupby('id').cumcount().add(1))
.pivot_table(index='id', columns='col', values='k', fill_value=0))
df_new.columns = [f"{x}_k" for x in df_new.columns]
print(df_new)
1_k 2_k
id
10450015 4.4 0
16510069 3.7 0
16690019 4.1 4

Related

How to transpose and Pandas DataFrame and name new columns?

I have simple Pandas DataFrame with 3 columns. I am trying to Transpose it into and then rename that new dataframe and I am having bit trouble.
df = pd.DataFrame({'TotalInvoicedPrice': [123],
'TotalProductCost': [18],
'ShippingCost': [5]})
I tried using
df =df.T
which transpose the DataFrame into:
TotalInvoicedPrice,123
TotalProductCost,18
ShippingCost,5
So now i have to add column names to this data frame "Metrics" and "Values"
I tried using
df.columns["Metrics","Values"]
but im getting errors.
What I need to get is DataFrame that looks like:
Metrics Values
0 TotalInvoicedPrice 123
1 TotalProductCost 18
2 ShippingCost 5
Let's reset the index then set the column labels
df.T.reset_index().set_axis(['Metrics', 'Values'], axis=1)
Metrics Values
0 TotalInvoicedPrice 123
1 TotalProductCost 18
2 ShippingCost 5
Maybe you can avoid transpose operation (little performance overhead)
#YOUR DATAFRAME
df = pd.DataFrame({'TotalInvoicedPrice': [123],
'TotalProductCost': [18],
'ShippingCost': [5]})
#FORM THE LISTS FROM YOUR COLUMNS AND FIRST ROW VALUES
l1 = df.columns.values.tolist()
l2 = df.iloc[0].tolist()
#CREATE A DATA FRAME.
df2 = pd.DataFrame(list(zip(l1, l2)),columns = ['Metrics', 'Values'])
print(df2)

How to create a generalized function in Python to compare consecutive columns in pandas df for more that 100 columns

I have two data frames DF1 and DF2 with more that 280+ columns in both df, I have to compare both df on some unique key , so I am merging both df first ,
Compare = pd.merge(df1,df2,how='outer',on='unique_key',suffix=('_X','_Y'))
now , I want to compare consecutive columns like
Compare['compare_1'] = Compare[_X]==Compare[_Y].
But, since columns are more than 280+ so I cant create compare columns for each set individually, I am looking for a function which can compare these consecutive columns.
I tried something like this,
col=df.columns
for x,i in enumerate(col):
for y,j in enumerate(col):
if y-x==1 and i!=j:
bina = df[i]-df[j]
df['MOM_' + str(j) + '_' + str(i)] = bina
But , it is not working as my df are huge more that 100k records and loops are making it complex.
IIUC use:
df1 = Compare.like('_X')
df1.columns = df1.columns.str.replace('_X$','')
df2 = Compare.like('_Y')
df2.columns = df2.columns.str.replace('_Y$','')
out = Compare.join(df1.sub(df2).add_prefix('MOM_'))

Convert lists present in each column to its respective datatypes

I have a sample dataframe as given below.
import pandas as pd
data = {'ID':['A', 'B', 'C', 'D],
'Age':[[20], [21], [19], [24]],
'Sex':[['Male'], ['Male'],['Female'], np.nan],
'Interest': [['Dance','Music'], ['Dance','Sports'], ['Hiking','Surfing'], np.nan]}
df = pd.DataFrame(data)
df
Each of the columns are in list datatype. I want to remove those lists and preserve the datatypes present within the lists for all columns.
The final output should look something shown below.
Any help is greatly appreciated. Thank you.
Option 1. You can use the .str column accessor to index the lists stored in the DataFrame values (or strings, or any other iterable):
# Replace columns containing length-1 lists with the only item in each list
df['Age'] = df['Age'].str[0]
df['Sex'] = df['Sex'].str[0]
# Pass the variable-length list into the join() string method
df['Interest'] = df['Interest'].apply(', '.join)
Option 2. explode Age and Sex, then apply ', '.join to Interest:
df = df.explode(['Age', 'Sex'])
df['Interest'] = df['Interest'].apply(', '.join)
Both options return:
df
ID Age Sex Interest
0 A 20 Male Dance, Music
1 B 21 Male Dance, Sports
2 C 19 Female Hiking, Surfing
EDIT
Option 3. If you have many columns which contain lists with possible missing values as np.nan, you can get the list-column names and then loop over them as follows:
# Get columns which contain at least one python list
list_cols = [c for c in df
if df[c].apply(lambda x: isinstance(x, list)).any()]
list_cols
['Age', 'Sex', 'Interest']
# Process each column
for c in list_cols:
# If all lists in column c contain a single item:
if (df[c].str.len() == 1).all():
df[c] = df[c].str[0]
else:
df[c] = df[c].apply(', '.join)

How to compare two different dataframes in Pandas and set the corresponding values?

I have two different size dataframes:
df1:
Name All
L_LV-SWB_1 10.300053
L_SWB_1-SWB_2 6.494196
L_SWB_2-SWB_3 4.738036
df2:
I want to create a new column in df2 called 'Isc' which contains the numerical values of df1, only if the 'On Element' column in df2 matches the 'Name' column in df1. Otherwise a 0 value is set.
First, match the name of the column with the same value,
df1.columns = ['On element','All']
print(df1)
On element All
0 L_LV-SWB_1 10.300053
1 L_SWB_1-SWB_2 6.494196
2 L_SWB_2-SWB_3 4.738036
You just need to merge that value(On element) as a key.
df = pd.merge(df2,df1,on='On element', how='left').fillna(0)
print(df)
Name On element All
0 CB_1-1 L_LV-SWB_1 10.300053
1 CB_1-2 L_SWB_1-SWB_2 6.494196
2 CB_2 L_LV-SWB_1 10.300053
3 CB_2-1 L_SWB_1-SWB_3 0

Using List Comprehension with Pandas Series and Dataframes

I have written the below code that accepts a pandas series (dataframe column) of strings and a dictionary of terms to replace in the strings.
def phrase_replace(repl_dict, str_series):
for k,v in repl_dict.items():
str_series = str_series.str.replace(k,v)
return str_series
It works correctly, but it seems like I should be able to use some kind of list comprehension instead of the for loop.
I don't want to use str_series = [] or {} because I don't want a list or a dictionary returned, but a pandas.core.series.Series
Likewise, if I want to use the function on every column in a dataframe:
for column in df.columns:
df[column] = phrase_replace(repl_dict, df[column])
There must be a list comprehension method to do this?
It is possible, but then need concat for DataFrame because get list of Series:
df = pd.concat([phrase_replace(repl_dict, df[column]) for column in df.columns], axis=1)
But maybe need replace by dictionary:
df = df.replace(repl_dict)
df = pd.DataFrame({'words':['apple','banana','orange']})
repl_dict = {'an':'foo', 'pp':'zz'}
df.replace({'words':repl_dict}, inplace=True, regex=True)
df
Out[263]:
words
0 azzle
1 bfoofooa
2 orfooge
If you want to apply to all columns:
df2 = pd.DataFrame({'key1':['apple', 'banana', 'orange'], 'key2':['banana', 'apple', 'pineapple']})
df2
Out[13]:
key1 key2
0 apple banana
1 banana apple
2 orange pineapple
df2.replace(repl_dict,inplace=True, regex=True)
df2
Out[15]:
key1 key2
0 azzle bfoofooa
1 bfoofooa azzle
2 orfooge pineazzle
The whole point of pandas is to not use for loops... it's optimized to use the built in methods for dataframes and series...

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