I am new to python and I am working on a requirement to list all unique value in a categorical column along with frequency of each value and the % frequency of each value in a column and using a for loop to perform it on the complete dataset. Also I am not sure if I have to use pd.Series to append data into a dataframe as per the screenshot attached because the length of the columns are different based on the unique values in a column.
Appreciate your help.
The below is the code I tried to work out but I am not able to workout on the other columns for unique value and % of frequency and create it as a data frame so that I can export it to CSV
Count_df = []
for item in df.columns:
Count_df_ = pd.DataFrame(df1[item].value_counts())
Count_df.append(Count_df_)
Count_dfdf = pd.DataFrame(Count_df)
Count_dfdf
Count_dfdf.to_csv(path_or_buf = Output + '_' + 'Count_.csv')
The input and Output expected is as below and the same is attached as an :
[Input data and expected Output][1]
Thanks in advance
No magic. Just append the output DataFrame column-by-column patiently.
Here I assume a 4-columned output in a single .csv
file. Based on personal work experience, this format is more handy than separate files for spreadsheet softwares. However, separated output is also possible within in the loop.
Code:
import pandas as pd
# please provide copy-able sample data next time
df = pd.DataFrame(
data={
"Name": ["A", "B", "C", "C", "A", "F"],
"col2": [True, False, False, False, False, True],
"col3": [1, 2, 3, 1, 1, 3],
}
)
# Construct an empty dataframe with convenient column order.
# The ordering can be adjusted later on.
df_ans = pd.DataFrame(
data={
"var_name": [],
"var_count": [],
"var_freq": [],
"col_name": [],
}
)
# process each column
for col in df.columns:
# get variable name and count
df_col_count = df[col].value_counts().to_frame().reset_index()
# rename columns
df_col_count.columns = ["var_name", "var_count"]
# compute frequency
df_col_count["var_freq"] = df_col_count["var_count"] / df_col_count["var_count"].sum()
# append column name
df_col_count["col_name"] = col
# sort (optional)
# (1) by name
df_col_count.sort_values(by="var_name", inplace=True)
# (2) by descending frequency
# df_col_count.sort_values(by="var_freq", ascending=False, inplace=True)
# append
df_ans = df_ans.append(df_col_count)
# For separated CSV output, output here (and "col_name" can be removed)
#df_col_count.to_csv(f"/path/to/{col}_freq.csv")
# reorder columns
df_ans = df_ans[["col_name", "var_name", "var_count", "var_freq"]]
# reindex
df_ans.reset_index(drop=True, inplace=True)
# write csv
# df_ans.to_csv(f"/path/to/all_freq.csv")
Output
# Each column (variable) is sorted by name.
df_ans
Out[12]:
col_name var_name var_count var_freq
0 Name A 2.0 0.333333
1 Name B 1.0 0.166667
2 Name C 2.0 0.333333
3 Name F 1.0 0.166667
4 col2 False 4.0 0.666667
5 col2 True 2.0 0.333333
6 col3 1 3.0 0.500000
7 col3 2 1.0 0.166667
8 col3 3 2.0 0.333333