UPI Payments VS Card Payments VS Cash Payments¶

In [1]:
import pandas as pd
df = pd.read_csv(r"C:\Users\lakshita rawat\Downloads\Jupyter_files\rbi_processed_2.csv")
df.dropna()
Out[1]:
Month Year UPI Transactions-Volume(Lakh) UPI Transactions-Value(Crores) Card Payments-Volume(Lakh) Card Payments-Value(Crores) Cash Payments-Volume(Lakh) Cash Payments-Value(Crores)
0 Jun 2025 183950 2403931 5673 218552 4382 229907
1 May 2025 186775 2514297 5827 226810 4605 246201
2 Apr 2025 178934 2394926 5674 222351 4603 244747
3 Mar 2025 183015 2477222 5790 240549 4984 263892
4 Feb 2025 161062 2196482 5052 201522 4498 235618
... ... ... ... ... ... ... ... ...
63 Mar 2020 12468 206462 5247 98783 5440 248574
64 Feb 2020 13257 222517 6129 120708 6185 283280
65 Jan 2020 13050 216243 6518 129444 6533 295573
66 Dec 2019 13084 202521 6485 128488 6491 290918
67 Nov 2019 12188 189229 5943 118339 6273 282631

68 rows × 8 columns

In [12]:
print(df.shape) #total rows and columns
print()

print(df.columns)
print()

print(df.isnull().sum())
print()

print(df.duplicated().sum())
print()

print(df.describe())
print()

print(df.info())

(68, 8)

Index(['Month', 'Year', 'UPI Transactions-Volume(Lakh)',
       'UPI Transactions-Value(Crores)', 'Card Payments-Volume(Lakh)',
       'Card Payments-Value(Crores)', 'Cash Payemnts-Volume(Lakh)',
       'Cash Payemnts-Value(Crores)'],
      dtype='object')

Month                             0
Year                              0
UPI Transactions-Volume(Lakh)     0
UPI Transactions-Value(Crores)    0
Card Payments-Volume(Lakh)        0
Card Payments-Value(Crores)       0
Cash Payemnts-Volume(Lakh)        0
Cash Payemnts-Value(Crores)       0
dtype: int64

0

              Year  UPI Transactions-Volume(Lakh)  \
count    68.000000                      68.000000   
mean   2022.176471                   77995.191176   
std       1.683468                   55448.362412   
min    2019.000000                    9996.000000   
25%    2021.000000                   26131.250000   
50%    2022.000000                   66813.500000   
75%    2024.000000                  121277.750000   
max    2025.000000                  186775.000000   

       UPI Transactions-Value(Crores)  Card Payments-Volume(Lakh)  \
count                    6.800000e+01                   68.000000   
mean                     1.188774e+06                 5187.367647   
std                      7.381061e+05                  588.618720   
min                      1.511410e+05                 2822.000000   
25%                      4.925245e+05                 4950.500000   
50%                      1.094800e+06                 5225.000000   
75%                      1.831173e+06                 5517.500000   
max                      2.514297e+06                 6518.000000   

       Card Payments-Value(Crores)  Cash Payemnts-Volume(Lakh)  \
count                    68.000000                   68.000000   
mean                 168209.279412                 5394.573529   
std                   45516.696054                  604.287648   
min                   44015.000000                 2950.000000   
25%                  130040.250000                 5068.250000   
50%                  178453.500000                 5544.000000   
75%                  203238.250000                 5735.750000   
max                  248709.000000                 6533.000000   

       Cash Payemnts-Value(Crores)  
count                    68.000000  
mean                 261045.308824  
std                   25760.992143  
min                  129098.000000  
25%                  252337.750000  
50%                  265439.500000  
75%                  278022.750000  
max                  295573.000000  

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 68 entries, 0 to 67
Data columns (total 8 columns):
 #   Column                          Non-Null Count  Dtype 
---  ------                          --------------  ----- 
 0   Month                           68 non-null     object
 1   Year                            68 non-null     int64 
 2   UPI Transactions-Volume(Lakh)   68 non-null     int64 
 3   UPI Transactions-Value(Crores)  68 non-null     int64 
 4   Card Payments-Volume(Lakh)      68 non-null     int64 
 5   Card Payments-Value(Crores)     68 non-null     int64 
 6   Cash Payemnts-Volume(Lakh)      68 non-null     int64 
 7   Cash Payemnts-Value(Crores)     68 non-null     int64 
dtypes: int64(7), object(1)
memory usage: 4.4+ KB
None
In [16]:
df = df.rename(columns={
    'UPI Transactions-Volume(Lakh)': 'UPI-Volume',
    'Card Payments-Volume(Lakh)': 'Card-Volume',
    'Cash Payemnts-Volume(Lakh)': 'Cash-Volume'})
print(df.columns.tolist())

['Month', 'Year', 'UPI-Volume', 'UPI Transactions-Value(Crores)', 'Card-Volume', 'Card Payments-Value(Crores)', 'Cash-Volume', 'Cash Payemnts-Value(Crores)']

Compairing the total count of UPI, Cash and Card transactions¶

In [17]:
import pandas as pd
import matplotlib.pyplot as plt

# Group by Year and sum the volumes
yearly = df.groupby('Year')[
    ['UPI-Volume',
     'Card-Volume',
     'Cash-Volume']
].sum().reset_index()

# Calculate total and percentage share
yearly['Total'] = yearly[['UPI-Volume', 
                          'Card-Volume', 
                          'Cash-Volume']].sum(axis=1)

yearly['UPI Share (%)'] = (yearly['UPI-Volume'] / yearly['Total']) * 100
yearly['Card Share (%)'] = (yearly['Card-Volume'] / yearly['Total']) * 100
yearly['Cash Share (%)'] = (yearly['Cash-Volume'] / yearly['Total']) * 100

print(yearly[['Year', 'UPI Share (%)', 'Card Share (%)', 'Cash Share (%)']])

# Plot stacked area chart
plt.figure(figsize=(10,6))
plt.stackplot(yearly['Year'],
              yearly['UPI Share (%)'],
              yearly['Card Share (%)'],
              yearly['Cash Share (%)'],
              labels=['UPI', 'Card', 'Cash'],
              alpha=0.8)

plt.title("Share of UPI, Card, and Cash Payments (Volume %)")
plt.xlabel("Year")
plt.ylabel("Percentage Share")
plt.legend(loc="upper left")
plt.show()

   Year  UPI Share (%)  Card Share (%)  Cash Share (%)
0  2019      50.079264       24.627457       25.293278
1  2020      60.894343       19.199187       19.906470
2  2021      75.139287       12.111530       12.749183
3  2022      84.746680        7.339133        7.914186
4  2023      90.314988        4.498283        5.186729
5  2024      93.279271        3.364671        3.356058
6  2025      94.532605        2.980583        2.486811
No description has been provided for this image
Insights from the graph-¶

UPI has grown exponentially, overtaking both card and cash payments.Cash and card volumes show relatively stable or slower growth, UPI dominates the digital payment ecosystem, indicating strong consumer adoption and preference for fast, convenient, and low-cost transactions.

Examining whether the cash usage are declining while digital payments (UPI + Card) are increasing¶

In [34]:
import pandas as pd
import matplotlib.pyplot as plt

# Assuming df has: 'Year', 'UPI-Volume', 'Cash-Volume', 'Card-Volume'

# Aggregate by Year
yearly_data = df.groupby("Year")[["UPI-Volume", "Cash-Volume", "Card-Volume"]].sum().reset_index()

# Create Digital Payments column
yearly_data["Digital-Volume"] = yearly_data["UPI-Volume"] + yearly_data["Card-Volume"]

# Plot
plt.figure(figsize=(10,6))

# Digital Payments
plt.plot(yearly_data["Year"], yearly_data["Digital-Volume"], label="Digital Payments (UPI + Card)", marker="o", linewidth=2)
for x, y in zip(yearly_data["Year"], yearly_data["Digital-Volume"]):
    plt.text(x, y, f"{y:,}", ha="center", va="bottom", fontsize=9)

# Cash Payments
plt.plot(yearly_data["Year"], yearly_data["Cash-Volume"], label="Cash Payments", marker="o", linewidth=2, linestyle="--", color="red")
for x, y in zip(yearly_data["Year"], yearly_data["Cash-Volume"]):
    plt.text(x, y, f"{y:,}", ha="center", va="bottom", fontsize=9)

plt.title("Cash Usage vs Digital Payments (Yearly)", fontsize=14, weight="bold")
plt.xlabel("Year", fontsize=12)
plt.ylabel("Transaction Volume", fontsize=12)
plt.legend()
plt.grid(True, linestyle="--", alpha=0.6)

plt.tight_layout()
plt.show()
No description has been provided for this image
Insights from the graph-¶

The analysis shows that cash usage is steadily declining, while digital payments (UPI + Cards combined) are consistently increasing year by year. This shift highlights the growing adoption of digital modes over traditional cash, reflecting changing consumer behavior.

Caculating the proportion (%) of UPI, Cards, and Cash in Total Transactions Values¶

In [5]:
df = df.rename(columns={
    'UPI Transactions-Value(Crores)': 'UPI-Value',
    'Card Payments-Value(Crores)': 'Card-Value',
    'Cash Payments-Value(Crores)': 'Cash-Value'})
print(df.columns.tolist())

['Month', 'Year', 'UPI Transactions-Volume(Lakh)', 'UPI-Value', 'Card Payments-Volume(Lakh)', 'Card-Value', 'Cash Payments-Volume(Lakh)', 'Cash-Value']
In [82]:
import pandas as pd

# Step 1: Calculate total transaction value per row
df["Total-Value"] = df["UPI-Value"] + df["Card-Value"] + df["Cash-Value"]

# Step 2: Drop NaN rows if any
df = df.dropna()

# Step 3: Calculate percentages for each row
df["UPI-%"] = (df["UPI-Value"] / df["Total-Value"]) * 100
df["Card-%"] = (df["Card-Value"] / df["Total-Value"]) * 100
df["Cash-%"] = (df["Cash-Value"] / df["Total-Value"]) * 100

# Step 4: Group by year and take mean percentages
yearly = df.groupby("Year")[["UPI-%", "Card-%", "Cash-%"]].mean().reset_index()

# Final yearly proportion table
print(yearly)

     Year      UPI-%     Card-%     Cash-%
0  2019.0  32.312683  20.355177  47.332140
1  2020.0  44.548253  16.473410  38.978337
2  2021.0  59.647277  13.701220  26.651503
3  2022.0  69.958237  11.567489  18.474274
4  2023.0  76.294980   9.859160  13.845860
5  2024.0  81.303223   8.415398  10.281379
6  2025.0  83.629999   7.774241   8.595761
In [ ]:
import matplotlib.pyplot as plt

# Drop NaN rows
df = df.dropna()

plt.figure(figsize=(10,6))

# Stacked bar chart using percentages
plt.bar(yearly["Year"], yearly["UPI-%"], label="UPI")
plt.bar(yearly["Year"], yearly["Card-%"], bottom=yearly["UPI-%"], label="Cards")
plt.bar(yearly["Year"], yearly["Cash-%"], bottom=yearly["UPI-%"] + yearly["Card-%"], label="Cash")

plt.xlabel("Year")
plt.ylabel("Proportion (%)")
plt.title("Yearly Proportion of UPI, Cards, and Cash Transactions")
plt.legend()
plt.show()
No description has been provided for this image
Insights from the graph-¶

Initially, cash was the most preferred mode of transaction, but after COVID, digital adoption surged. UPI saw a sharp rise, quickly surpassing both cards and cash, and by 2025 it became the dominant payment method with over 80% share.

Analyzing the Spread of Transaction Values Across Years¶

In [11]:
import matplotlib.pyplot as plt
import seaborn as sns

# Assuming you already have a 'Year' column extracted from your dataset
plt.figure(figsize=(12,6))

# Convert data from wide → long format (for easy plotting with seaborn)
df_melted = df.melt(id_vars=["Year"], 
                    value_vars=["UPI-Value", "Cash-Value", "Card-Value"],
                    var_name="Payment Method", 
                    value_name="Transaction Value")

# Boxplot to show spread across years
sns.boxplot(data=df_melted, x="Year", y="Transaction Value", hue="Payment Method")

plt.title("Spread of Transaction Value Across Years by Payment Method")
plt.xlabel("Year")
plt.ylabel("Transaction Value (in Crores)")
plt.legend(title="Payment Method")
plt.xticks(rotation=45)
plt.show()
No description has been provided for this image
Insights from the graph-¶

The boxplot shows that UPI transactions not only dominate in terms of volume but also exhibit wider variation in earlier years, stabilizing over time as adoption became mainstream. In contrast, Cash and Card transactions remain minimal and consistent, squeezed near the lower end of the scale. The median labels reinforce how UPI has rapidly overtaken traditional payment modes in transaction value.