Tag: Pandas

Python Remove Accents Pandas Code Example

Snippet 1 In [60]: df[‘Country’].str.normalize(‘NFKD’).str.encode(‘ascii’, errors=’ignore’).str.decode(‘utf-8’) Out[60]: 0 Aland Islands 1 Aland Islands 2 Albania 3 Albania 4 Albania Name: Country, dtype: object Snippet 2 df[‘Country’] = df[‘Country’].str.replace(u”Å”, “A”) df[‘City’] = df[‘City’].str.replace(u”ë”, “e”) Copyright © Code Fetcher 2020    

Python When To Use Pandas Series, Numpy Ndarrays Or Simply Python Dictionaries Code Example

Snippet 1 # A useful rule of thumb is to use the simplest data structure that still # satisfies your needs. If we rank the data structures from most simple # to least simple, it usually ends up like this: 1. Dictionaries / lists 2. Numpy arrays 3. Pandas series / dataframes # See source… Continue reading Python When To Use Pandas Series, Numpy Ndarrays Or Simply Python Dictionaries Code Example

Pandas Reading Each Xlsx File In Folder Code Example

Snippet 1 import sys import csv import glob import pandas as pd # get data file names path =r’C:DRODCL_rawdata_filesexcelfiles’ filenames = glob.glob(path + “/*.xlsx”) dfs = [] for df in dfs: xl_file = pd.ExcelFile(filenames) df=xl_file.parse(‘Sheet1’) dfs.concat(df, ignore_index=True) Snippet 2 import os import pandas as pd import openpyxl as excel import glob #setting up path path… Continue reading Pandas Reading Each Xlsx File In Folder Code Example

Read All Files And Store In One Dataframe Pandas Code Example

Snippet 1 path = r’C:DRODCL_rawdata_files’ # use your path all_files = glob.glob(os.path.join(path, “*.csv”)) # advisable to use os.path.join as this makes concatenation OS independent df_from_each_file = (pd.read_csv(f) for f in all_files) concatenated_df = pd.concat(df_from_each_file, ignore_index=True) Copyright © Code Fetcher 2020    

Pandas Replace Nan Code Example

Snippet 1 data[“Gender”].fillna(“No Gender”, inplace = True) Snippet 2 In [7]: df Out[7]: 0 1 0 NaN NaN 1 -0.494375 0.570994 2 NaN NaN 3 1.876360 -0.229738 4 NaN NaN In [8]: df.fillna(0) Out[8]: 0 1 0 0.000000 0.000000 1 -0.494375 0.570994 2 0.000000 0.000000 3 1.876360 -0.229738 4 0.000000 0.000000 Snippet 3 df.replace(np.nan,0) Snippet… Continue reading Pandas Replace Nan Code Example

Pandas Reset Index Examples

dimRegion2 = pd.io.sql.get_schema(dimRegion2.reset_index(),’dimRegion2′) source movie_overview_2022 = movie_overview_2022.reset_index(drop=True) source seasons_2022_2013.reset_index(drop = True, inplace = True) source submission = pd.read_csv(“submission_lgbm.csv”, header=None).reset_index() source Copyright © Code Fetcher 2022    

Pandas Select Rows By Multiple Conditions Code Example

Snippet 1 # Create variable with TRUE if nationality is USA american = df[‘nationality’] == “USA” # Create variable with TRUE if age is greater than 50 elderly = df[‘age’] > 50 # Select all cases where nationality is USA and age is greater than 50 df[american & elderly] Snippet 2 >>> df[“A”][(df[“B”] > 50)… Continue reading Pandas Select Rows By Multiple Conditions Code Example

Pandas Sum Multiple Columns Groupby Code Example

Snippet 1 #UPDATED (June 2020): Introduced in Pandas 0.25.0, #Pandas has added new groupby behavior “named aggregation” and tuples, #for naming the output columns when applying multiple aggregation functions #to specific columns. df.groupby( [‘col1′,’col2’] ).agg( sum_col3 = (‘col3′,’sum’), sum_col4 = (‘col4′,’sum’), ).reset_index() Snippet 2 df.groupby([‘col1′,’col2’]).agg({‘col3′:’sum’,’col4′:’sum’}).reset_index() Similar Snippets Pandas Ttable With Sum Totals Code Example –… Continue reading Pandas Sum Multiple Columns Groupby Code Example