I just started to using pandas and I would to reduce amount of data that I get by merging my DataFrames in that way:
Load df
Check in which columns all values are the same
Delete other columns
Reduce df to single Series
Return
def merge_df(in_df):
alist = []
for col in in_df.columns:
if len(in_df[col].unique()) == 1:
alist.append(col)
return in_df[alist].T.squeeze()[1]
Is there any more elegent way to do it? E.g. without looping through all columns?
Yeah you can remove duplicate data by pandas simple function.
df.drop_duplicates()
You can refer documentation here.
For removing particular column redundant data you can pass column name as a parameter "subset". It will remove whole row for duplicate data.
Related
I have a dataframe in Pandas where I would like to turn the values of a set of columns (more specifically, from column index 3 to the end) into a new single column that contains a list of those values in each row.
Right now, I have code that can print out a list of the values in the columns, but only for single row. How can I do this for the whole dataframe?
import pandas as pd
orig_df = pd.read_csv('zipcode_price_dataset.csv')
df = orig_df.loc[(orig_df['State'] == "CA")]
row = df.head(1)
print(row[df.columns[3:].values].values[0])
I could iterate through the rows using a for loop, but is there a more concise way to do this?
Something like the following:
df['new'] = df[df.columns[3:]].values.tolist()
Use .iloc:
df.iloc[: , 3:].agg(list, axis=1)
I have a rather messy dataframe in which I need to assign first 3 rows as a multilevel column names.
This is my dataframe and I need index 3, 4 and 5 to be my multiindex column names.
For example, 'MINERAL TOTAL' should be the level 0 until next item; 'TRATAMIENTO (ts)' should be level 1 until 'LEY Cu(%)' comes up.
What I need actually is try to emulate what pandas.read_excel does when 'header' is specified with multiple rows.
Please help!
I am trying this, but no luck at all:
pd.DataFrame(data=df.iloc[3:, :].to_numpy(), columns=tuple(df.iloc[:3, :].to_numpy(dtype='str')))
You can pass a list of row indexes to the header argument and pandas will combine them into a MultiIndex.
import pandas as pd
df = pd.read_excel('ExcelFile.xlsx', header=[0,1,2])
By default, pandas will read in the top row as the sole header row. You can pass the header argument into pandas.read_excel() that indicates how many rows are to be used as headers. This can be either an int, or list of ints. See the pandas.read_excel() documentation for more information.
As you mentioned you are unable to use pandas.read_excel(). However, if you do already have a DataFrame of the data you need, you can use pandas.MultiIndex.from_arrays(). First you would need to specify an array of the header rows which in your case would look something like:
array = [df.iloc[0].values, df.iloc[1].values, df.iloc[2].values]
df.columns = pd.MultiIndex.from_arrays(array)
The only issue here is this includes the "NaN" values in the new MultiIndex header. To get around this, you could create some function to clean and forward fill the lists that make up the array.
Although not the prettiest, nor the most efficient, this could look something like the following (off the top of my head):
def forward_fill(iterable):
return pd.Series(iterable).ffill().to_list()
zero = forward_fill(df.iloc[0].to_list())
one = forward_fill(df.iloc[1].to_list())
two = one = forward_fill(df.iloc[2].to_list())
array = [zero, one, two]
df.columns = pd.MultiIndex.from_arrays(array)
You may also wish to drop the header rows (in this case rows 0 and 1) and reindex the DataFrame.
df.drop(index=[0,1,2], inplace=True)
df.reset_index(drop=True, inplace=True)
Since columns are also indices, you can just transpose, set index levels, and transpose back.
df.T.fillna(method='ffill').set_index([3, 4, 5]).T
I just edited the question as maybe I didn't make myself clear.
I have two dataframes (MR and DT)
The column 'A' in dataframe DT is a subset of the column 'A' in dataframe MR, they both are just similar (not equal) in this ID column, the rest of the columns are different as well as the number of rows.
How can I get the rows from dataframe MR['ID'] that are equal to the dataframe DT['ID']? Knowing that values in 'ID' can appear several times in the same column.
The DT is 1538 rows and MR is 2060 rows).
I tried some lines proposed here >https://stackoverflow.com/questions/28901683/pandas-get-rows-which-are-not-in-other-dataframe but I got bizarre results as I don't fully understand the methods they proposed (and the goal is little different)
Thanks!
Take a look at pandas.Series.isin() method. In your case you'd want to use something like:
matching_id = MR.ID.isin(DT.ID) # This returns a boolean Series of whether values match or not
# Now filter your dataframe to keep only matching rows
new_df = MR.loc[matching_id, :]
Or if you want to just get a new dataframe of combined records for the same ID you need to use merge():
new_df = pd.merge(MR, DT, on='ID')
This will create a new dataframe with columns from both original dfs but only where ID is the same.
I see a lot of questions related to dropping rows that have a certain value in a column, or dropping the entirety of columns, but pretend we have a Pandas Dataframe like the one below.
In this case, how could one write a line to go through the CSV, and drop all rows like 2 and 4? Thank you.
You could try
~((~df).all(axis=1))
to get the rows that you want to keep/drop. To get the dataframe with just those rows, you would use
df = df[~((~df).all(axis=1))]
A more detailed explanation is here:
Delete rows from a pandas DataFrame based on a conditional expression involving len(string) giving KeyError
This should help
for i in range(df.shape[0]):
value=df.shape[1]
count=0
for column_name in column_names:
if df.loc[[i]].column_name==False:
count=count+1
if count==value:
df.drop(index=i,inplace=True)
I have a csv file with repeated group of columns and I want to convert the repeated group of columns to only one column each.
I know for this kind of problem we can use the function melt in python but only when having repeated columns of only one variable .
I already found a simple solution for my problem , but I don't think it's the best.I put the repeated columns of every variable into a list,then all repeated variables into bigger list.
Then when iterating the list , I use melt on every variable(list of repeated columns of same group).
Finally I concatenate the new dataframes to only one dataframe.
Here is my code:
import pandas as pd
file_name='file.xlsx'
df_final=pd.DataFrame()
#create lists to hold headers & other variables
HEADERS = []
A = []
B=[]
C=[]
#Read CSV File
df = pd.read_excel(file_name, sheet_name='Sheet1')
#create a list of all the columns
columns = list(df)
#split columns list into headers and other variables
for col in columns:
if col.startswith('A'):
A.append(col)
elif col.startswith('B'):
B.append(col)
elif col.startswith('C') :
C.append(col)
else:
HEADERS.append(col)
#For headers take into account only the first 17 variables
HEADERS=HEADERS[:17]
#group column variables
All_cols=[]
All_cols.append(A)
All_cols.append(B)
All_cols.append(C)
#Create a final DF
for list in All_cols:
df_x = pd.melt(df,
id_vars=HEADERS,
value_vars=list,
var_name=list[0],
value_name=list[0]+'_Val')
#Concatenate DataFrames 1
df_final= pd.concat([df_A, df_x],axis=1)
#Delete duplicate columns
df_final= df_final.loc[:, ~df_final.columns.duplicated()]
I want to find a better maintenable solution for my problem and I want to have a dataframe for every group of columns (same variable) as a result.
As a beginner in python , I can't find a way of doing this.
I'm joining an image that explains what I want in case I didn't make it clear enough.
joined image