Pandas newbie, looking for suggestion for improvement











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The following works, but seems to me to be overly complex. Is there an easier way to calculate time differences and calculate summary statistics? I especially am looking to replace the for loop



import pandas as pd
import numpy as np

# Read in the csv file using the 'record_id' field as the index, keeping only the timestamp
df = pd.read_csv("my_data.csv", sep=',', index_col='record_id', usecols=["record_id", "timestamp"])

# Group them by record_id
record_id_grouping = df.groupby("record_id")

# Create a list of data frames, each with a different record_id
df_list = [x for _, x in record_id_grouping]

new_df_list =

# Iterate over the list of data frames
for df in df_list:
# Add a time difference column
df['diff'] = df["timestamp"].diff()
# Drop the timestamp column and any data frame rows with NaN
df = df.loc[:,["diff"]].dropna()
# Append the new data frame to a new list
new_df_list.append(df)

# Remove any data frames from the list that are empty
new_df_list = [df for df in new_df_list if df.empty == False]

# Put all the data frames in the list back into a single data frame
new_df = pd.concat(new_df_list)

# Calculate mean, std, max, min and count for each record_id in the data frame
final_df = new_df.groupby("record_id").agg(['mean', 'std', 'max', 'min', 'count'])

# Drop the diff level
final_df.columns = final_df.columns.droplevel()

# Drop any rows that have Nan in them.
final_df = final_df.dropna()








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    The following works, but seems to me to be overly complex. Is there an easier way to calculate time differences and calculate summary statistics? I especially am looking to replace the for loop



    import pandas as pd
    import numpy as np

    # Read in the csv file using the 'record_id' field as the index, keeping only the timestamp
    df = pd.read_csv("my_data.csv", sep=',', index_col='record_id', usecols=["record_id", "timestamp"])

    # Group them by record_id
    record_id_grouping = df.groupby("record_id")

    # Create a list of data frames, each with a different record_id
    df_list = [x for _, x in record_id_grouping]

    new_df_list =

    # Iterate over the list of data frames
    for df in df_list:
    # Add a time difference column
    df['diff'] = df["timestamp"].diff()
    # Drop the timestamp column and any data frame rows with NaN
    df = df.loc[:,["diff"]].dropna()
    # Append the new data frame to a new list
    new_df_list.append(df)

    # Remove any data frames from the list that are empty
    new_df_list = [df for df in new_df_list if df.empty == False]

    # Put all the data frames in the list back into a single data frame
    new_df = pd.concat(new_df_list)

    # Calculate mean, std, max, min and count for each record_id in the data frame
    final_df = new_df.groupby("record_id").agg(['mean', 'std', 'max', 'min', 'count'])

    # Drop the diff level
    final_df.columns = final_df.columns.droplevel()

    # Drop any rows that have Nan in them.
    final_df = final_df.dropna()








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      down vote

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      The following works, but seems to me to be overly complex. Is there an easier way to calculate time differences and calculate summary statistics? I especially am looking to replace the for loop



      import pandas as pd
      import numpy as np

      # Read in the csv file using the 'record_id' field as the index, keeping only the timestamp
      df = pd.read_csv("my_data.csv", sep=',', index_col='record_id', usecols=["record_id", "timestamp"])

      # Group them by record_id
      record_id_grouping = df.groupby("record_id")

      # Create a list of data frames, each with a different record_id
      df_list = [x for _, x in record_id_grouping]

      new_df_list =

      # Iterate over the list of data frames
      for df in df_list:
      # Add a time difference column
      df['diff'] = df["timestamp"].diff()
      # Drop the timestamp column and any data frame rows with NaN
      df = df.loc[:,["diff"]].dropna()
      # Append the new data frame to a new list
      new_df_list.append(df)

      # Remove any data frames from the list that are empty
      new_df_list = [df for df in new_df_list if df.empty == False]

      # Put all the data frames in the list back into a single data frame
      new_df = pd.concat(new_df_list)

      # Calculate mean, std, max, min and count for each record_id in the data frame
      final_df = new_df.groupby("record_id").agg(['mean', 'std', 'max', 'min', 'count'])

      # Drop the diff level
      final_df.columns = final_df.columns.droplevel()

      # Drop any rows that have Nan in them.
      final_df = final_df.dropna()








      share







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      ACRL is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      The following works, but seems to me to be overly complex. Is there an easier way to calculate time differences and calculate summary statistics? I especially am looking to replace the for loop



      import pandas as pd
      import numpy as np

      # Read in the csv file using the 'record_id' field as the index, keeping only the timestamp
      df = pd.read_csv("my_data.csv", sep=',', index_col='record_id', usecols=["record_id", "timestamp"])

      # Group them by record_id
      record_id_grouping = df.groupby("record_id")

      # Create a list of data frames, each with a different record_id
      df_list = [x for _, x in record_id_grouping]

      new_df_list =

      # Iterate over the list of data frames
      for df in df_list:
      # Add a time difference column
      df['diff'] = df["timestamp"].diff()
      # Drop the timestamp column and any data frame rows with NaN
      df = df.loc[:,["diff"]].dropna()
      # Append the new data frame to a new list
      new_df_list.append(df)

      # Remove any data frames from the list that are empty
      new_df_list = [df for df in new_df_list if df.empty == False]

      # Put all the data frames in the list back into a single data frame
      new_df = pd.concat(new_df_list)

      # Calculate mean, std, max, min and count for each record_id in the data frame
      final_df = new_df.groupby("record_id").agg(['mean', 'std', 'max', 'min', 'count'])

      # Drop the diff level
      final_df.columns = final_df.columns.droplevel()

      # Drop any rows that have Nan in them.
      final_df = final_df.dropna()






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      asked 3 mins ago









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      Check out our Code of Conduct.






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